Claims:We Claim: 1. A processor implemented method for signal pre-processing based on a plurality of data driven models and a data dependent model transformation, comprising: receiving, a raw signal as an input; learning, a set of representational basis from the received raw signal, wherein the set of representational basis comprises a plurality of orthonormal vectors; selecting, at least one orthonormal vector from the plurality of orthonormal vectors, wherein at least one selection of the orthonormal vector corresponds to a plurality of dictionary atoms, based on a domain dependent time-frequency energy distribution pattern; determining, a structure of the plurality of dictionary atoms, wherein structure of the plurality of dictionary atoms corresponds to a graph structure represented as a Laplacian matrix (L); integrating, the graph structure as a structure of the set of representational basis to obtain a reconfigured data model; and reconstructing, using the reconfigured data model to obtain a denoised signal, wherein at least one of constraints on a optimization problem corresponds to desired spectral and topological structure. 2. The processor implemented method of claim 1, wherein the structure corresponds to a spectral structure and topological structure of the plurality of dictionary atoms and the topological structure of the synthesis coefficients. 3. The processor implemented method of claim 1, wherein a structure of a reconstructed signal is determined based on at least one of (a) time-frequency pattern, (b) graph structured dictionary atoms and combination thereof. 4. The processor implemented method of claim 1, wherein a penalty factor determines the statistical, spectral and topological structure of reconstructed signal encoded in factor lambda ?2. 5. The processor implemented method of claim 4, wherein the penalty factor controls a tradeoff between approximation capability of the dictionary and structure inducing strength of a graph Laplacian penalty. 6. The processor implemented method of claim 1, wherein reconstruction of the signal is performed by estimating graph structured coefficients of a graph and time-frequency structured dictionary. 7. The processor implemented method of claim 1, wherein the denoised signal is validated if ratio of the sum of the major eigenmodes to the sum of the minor eigenmodes is comparable to a domain dependent threshold for clean signals in the domain. 8. A system (100) to determine service dependency based on network packets traces, wherein the system comprising: a memory (102) storing instructions; one or more communication interfaces (106); and one or more hardware processors (104) coupled to the memory (102) via the one or more communication interfaces (106), wherein the one or more hardware processors (104) are configured by the instructions to: receive, a raw signal as an input; learn, a set of representational basis from the received raw signal, wherein the set of representational basis comprises a plurality of orthonormal vectors; select, at least one orthonormal vector from the plurality of orthonormal vectors, wherein at least one selection of the orthonormal vector corresponds to a plurality of dictionary atoms, based on a domain dependent time-frequency energy distribution pattern; determine, a structure of the plurality of dictionary atoms, wherein structure of the plurality of dictionary atoms corresponds to a graph structure represented as a Laplacian matrix (L); integrate, the graph structure as a structure of the set of representational basis to obtain a reconfigured data model; and reconstruct, using the reconfigured data model to obtain a denoised signal, wherein at least one of constraints on a optimization problem to desired spectral and topological structure. 9. The system of claim 8, wherein the structure corresponds to a spectral structure and topological structure of the plurality of dictionary atoms and the topological structure of the synthesis coefficients. 10. The system of claim 8, wherein a structure of a reconstructed signal is determined based on at least one of (a) time-frequency pattern, (b) graph structured dictionary atoms and combination thereof. 11. The system of claim 8, wherein a penalty factor determines the statistical, spectral and topological structure of reconstructed signal encoded in factor lambda ?2. 12. The system of claim 11, wherein the penalty factor controls a tradeoff between approximation capability of the dictionary and structure inducing strength of a graph Laplacian penalty. 13. The system of claim 8, wherein reconstruction of the signal is performed by estimating graph structured coefficients of a graph and time-frequency structured dictionary. 14. The system of claim 8, wherein the denoised signal is validated if ratio of the sum of the major eigenmodes to the sum of the minor eigenmodes is comparable to a domain dependent threshold for clean signals in the domain Dated this 20th day of March 2019. Tata Consultancy Services Limited By their Agent & Attorney (Adheesh Nargolkar) of Khaitan & Co Reg No IN/PA-1086 , Description:FORM 2 THE PATENTS ACT, 1970 (39 of 1970) & THE PATENT RULES, 2003 COMPLETE SPECIFICATION (See Section 10 and Rule 13) Title of invention: SYSTEM AND METHOD FOR SIGNAL PRE-PROCESSING BASED ON DATA DRIVEN MODELS AND DATA DEPENDENT MODEL TRANSFORMATION Applicant Tata Consultancy Services Limited A company Incorporated in India under the Companies Act, 1956 Having address: Nirmal Building, 9th floor, Nariman point, Mumbai 400021, Maharashtra, India Preamble to the description The following specification particularly describes the invention and the manner in which it is to be performed. TECHNICAL FIELD The disclosure herein generally relates to data processing, and, more particularly, to system and method for signal pre-processing based on data driven models and data dependent model transformation. BACKGROUND There is a pressing need for sophisticated computational tools for automating data pre-processing, management, and analytics, including scalable tools for storing, indexing, annotating, retrieving, organizing, assessing reliability of, and analyzing data. Typical applications include data processing in healthcare, transportation and manufacturing. Frameworks are needed that organize: (a) hypotheses that are under consideration, (b) data that supports them, (c) models that have been created from the data, and (d) the hypotheses resulting from the models. However, connection between domain knowledge and the analytics that can support inferences on data is often not thoughtfully captured and preserved in existing computational frameworks. The separation between knowledge and data makes it difficult for scientists to keep track of what hypotheses have been considered, what data supports them, what models have been created from the data, and how new hypotheses are formulated from those models. As more complex data becomes available with increasing volume, variety, and velocity, exploration of models becomes unmanageable. New computational approaches are needed to bridge the gap between knowledge and data and exploit them to facilitate scientists’ understanding of complex phenomena. Many efforts are being made to analyze data using commercially available tools or by developing an analysis tool that meets the requirements of a particular application. Some of these efforts have ignored the fact that problems exist with real world data and some form of data preprocessing is usually required to intelligently analyze the data. This means that commercial or research tools should provide data preprocessing facilities to be used before or during the actual data analysis process. Data preprocessing is a time consuming task. Growing amounts of data produced by modern process monitoring and data acquisition systems has resulted in correspondingly large data processing requirements and, therefore, efficient techniques for automatic data preprocessing are important. Data preprocessing may be performed on the data for the following reasons: a. solving data problems that may prevent us from performing any type of analysis on the data, b. understanding the nature of the data and performing a more meaningful data analysis, and c. extracting more meaningful knowledge from a given set of data. Traditional signal processing approaches typically only look at input/output relations. A holistic systems perspective which takes into account the end goal in mind for designing pre-processing techniques is not taken. This leads to a design of algorithms optimized only for a certain performance metric. The design of algorithms do not take into account certain domain dependent bounding constraints on the design objective, which can lead to a completely different solution when compared to a plain unconstrained design objective. SUMMARY Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one aspect, processor implemented method for signal pre-processing based on a plurality of data driven models and a data dependent model transformation is provided. The processor implemented method includes (a) receiving, a raw signal as an input; (b) learning, a set of representational basis from the received raw signal; (c) selecting, at least one orthonormal vector from the plurality of orthonormal vectors; (d) determining, a structure of the plurality of dictionary atoms; (e) integrating, the graph structure as a structure of the set of representational basis to obtain a reconfigured data model; and (f) reconstructing, using the reconfigured data model to obtain a denoised signal. In an embodiment, set of representational basis comprises a plurality of orthonormal vectors. In an embodiment, at least one selection of the orthonormal vector corresponds to a plurality of dictionary atoms, based on a domain dependent time-frequency energy distribution pattern. In an embodiment, structure of the plurality of dictionary atoms corresponds to a graph structure represented as a Laplacian matrix (L). In an embodiment, at least one of constraints on an optimization problem corresponds to desired spectral and topological structure. In an embodiment, the structure may corresponds to a spectral structure and topological structure of the plurality of dictionary atoms and the topological structure of the synthesis coefficients. In an embodiment, a structure of a reconstructed signal may be determined based on at least one of (a) time-frequency pattern, (b) graph structured dictionary atoms and combination thereof. In an embodiment, a penalty factor may determine the statistical, spectral and topological structure of reconstructed signal encoded in factor lambda ?2. In an embodiment, the penalty factor may controls a tradeoff between approximation capability of the dictionary and structure inducing strength of a graph Laplacian penalty. In an embodiment, reconstruction of the signal may be performed by estimating graph structured coefficients of a graph and time-frequency structured dictionary. In an embodiment, the denoised signal may be validated if ratio of the sum of the major eigenmodes to the sum of the minor eigenmodes is comparable to a domain dependent threshold for clean signals in the domain. In another aspect, there is provided a processor implemented system to signal pre-process based on a plurality of data driven models and a data dependent model transformation. The system comprises a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to: (a) receive, a raw signal as an input; (b) learn, a set of representational basis from the received raw signal; (c) select, at least one orthonormal vector from the plurality of orthonormal vectors; (d) determine, a structure of the plurality of dictionary atoms; (e) integrate, the graph structure as a structure of the set of representational basis to obtain a reconfigured data model; and (f) reconstruct, using the reconfigured data model to obtain a denoised signal. In an embodiment, set of representational basis comprises a plurality of orthonormal vectors. In an embodiment, at least one selection of the orthonormal vector corresponds to a plurality of dictionary atoms, based on a domain dependent time-frequency energy distribution pattern. In an embodiment, structure of the plurality of dictionary atoms corresponds to a graph structure represented as a Laplacian matrix (L). In an embodiment, at least one of constraints on an optimization problem corresponds to desired spectral and topological structure. In an embodiment, the structure may corresponds to a spectral structure and topological structure of the plurality of dictionary atoms and the topological structure of the synthesis coefficients. In an embodiment, a structure of a reconstructed signal may be determined based on at least one of (a) time-frequency pattern, (b) graph structured dictionary atoms and combination thereof. In an embodiment, a penalty factor may determine the statistical, spectral and topological structure of reconstructed signal encoded in factor lambda ?2. In an embodiment, the penalty factor may controls a tradeoff between approximation capability of the dictionary and structure inducing strength of a graph Laplacian penalty. In an embodiment, reconstruction of the signal may be performed by estimating graph structured coefficients of a graph and time-frequency structured dictionary. In an embodiment, the denoised signal may be validated if ratio of the sum of the major eigenmodes to the sum of the minor eigenmodes is comparable to a domain dependent threshold for clean signals in the domain. In yet another aspect, there are provided one or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors causes (a) receiving, a raw signal as an input; (b) learning, a set of representational basis from the received raw signal; (c) selecting, at least one orthonormal vector from the plurality of orthonormal vectors; (d) determining, a structure of the plurality of dictionary atoms; (e) integrating, the graph structure as a structure of the set of representational basis to obtain a reconfigured data model; and (f) reconstructing, using the reconfigured data model to obtain a denoised signal. In an embodiment, set of representational basis comprises a plurality of orthonormal vectors. In an embodiment, at least one selection of the orthonormal vector corresponds to a plurality of dictionary atoms, based on a domain dependent time-frequency energy distribution pattern. In an embodiment, structure of the plurality of dictionary atoms corresponds to a graph structure represented as a Laplacian matrix (L). In an embodiment, at least one of constraints on an optimization problem corresponds to desired spectral and topological structure. In an embodiment, the structure may corresponds to a spectral structure and topological structure of the plurality of dictionary atoms and the topological structure of the synthesis coefficients. In an embodiment, a structure of a reconstructed signal may be determined based on at least one of (a) time-frequency pattern, (b) graph structured dictionary atoms and combination thereof. In an embodiment, a penalty factor may determine the statistical, spectral and topological structure of reconstructed signal encoded in factor lambda ?2. In an embodiment, the penalty factor may controls a tradeoff between approximation capability of the dictionary and structure inducing strength of a graph Laplacian penalty. In an embodiment, reconstruction of the signal may be performed by estimating graph structured coefficients of a graph and time-frequency structured dictionary. In an embodiment, the denoised signal may be validated if ratio of the sum of the major eigenmodes to the sum of the minor eigenmodes is comparable to a domain dependent threshold for clean signals in the domain. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed. BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles: FIG. 1 illustrates a block diagram of a system for signal pre-processing based on data driven models and data dependent model transformation according to embodiments of the present disclosure. FIG. 2 is an exemplary signal pre-processing system for signal pre-processing based on data driven models and data dependent model transformation according to embodiments of the present disclosure. FIG. 3 is a block diagram illustrates a feedback structure for making decisions on a re-parameterization of the process according to embodiments of the present disclosure. FIG. 4A & 4B illustrates a flow diagram method for signal pre-processing based on a plurality of data driven models and a data dependent model transformation according to embodiments of the present disclosure. FIG. 5A is a graphical representation illustrates dynamical structure used to evaluate accuracy metric for a test signal according to embodiments of the present disclosure. FIG. 5B is a graphical representation illustrates spectral structure imposed on the data model (dictionary) according to embodiments of the present disclosure. FIG. 5C is a graphical representation illustrates topological structure imposed on the data model (dictionary) according to embodiments of the present disclosure. FIG. 5D is a graphical representation illustrates results of denoising and anomaly removal by reconstructing the test signal using spectral and topological structured dictionary according to embodiments of the present disclosure. DETAILED DESCRIPTION OF EMBODIMENTS Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the spirit and scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope and spirit being indicated by the following claims. The embodiments of the present disclosure provide a system and method for pre-processing including anomaly removal and denoising of analog signals. Domain knowledge is incorporated into the formulation of plurality of algorithms. The domain knowledge are obtained from expert knowledge of features of signals but not limited to which are encountered in a healthcare, a manufacturing, and a machinery monitoring. Data driven models are used which are based on a priori domain knowledge. Domain knowledge related to signals for a given application can be captured based on at least one of: (a) a knowledge of dynamics of a system/physical process generating signal, (b) a knowledge of statistical properties of the signal, (c) a knowledge of topological properties of the signal in terms of a connected graph, (d) a knowledge of signal energy distribution over time-frequency plane, indicative of the spectral properties of the signal, and (e) combination thereof. Data driven modeling in which signals are represented in terms of the data driven models, in contrast to apriori models (e.g. Fourier, Wavelet). In an embodiment, the data driven models are adaptive. The model dimension and model complexity can be changed to achieve a balance between representational accuracy and computational complexity in calculating inferences, while preserving essential features of the signal. Incorporating domain knowledge into the data driven models in which re-structuring/re-configuration of data driven models is performed with the domain dependent features to be preserved in the signal. In an embodiment, level of reconfiguration can be controlled to achieve desired statistical accuracy of inferences on the signal. Referring now to the drawings, and more particularly to FIG. 1 through 5D, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method. FIG. 1 illustrates a block diagram of a system 100 for signal pre-processing based on data driven models and data dependent model transformation according to embodiments of the present disclosure. In an embodiment, the system 100 includes one or more processors 104, communication interface device(s) or input/output (I/O) interface(s) 106, and one or more data storage devices or memory 102 operatively coupled to the one or more processors 104. The memory 102 comprises a database 108. The one or more processors 104 that are hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) is configured to fetch and execute computer-readable instructions stored in the memory. In an embodiment, the system 100 can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices, workstations, mainframe computers, servers, a network cloud and the like. The I/O interface device(s) 106 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an embodiment, the I/O interface device(s) can include one or more ports for connecting a number of devices to one another or to another server. The memory 102 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. The database 108 may store information but are not limited to, a plurality of parameters obtained from one or more sensors, wherein the plurality of parameters are specific to an entity (e.g., user, machine, and the like). Further, the database 108 stores information pertaining to inputs fed to the system 100 and/or outputs generated by the system (e.g., at each stage), specific to the methodology described herein. More specifically, the database 108 stores information being processed at each step of the proposed methodology. FIG. 2, with reference to FIG. 1, illustrates an exemplary signal pre-processing system 200 for signal pre-processing based on data driven models and data dependent model transformation according to embodiments of the present disclosure. The signal pre-processing system 200 which includes a plurality of states 202, a plurality of feature extraction unit 204A-204N, a fusion unit 206, an adaptation decision unit 208, a model unit 210, an inference unit 212, a control unit 214, and a plant 216. In an embodiment, the plant 216 is an entity which has a state space. The states are corresponding to the multiple features of a signal e.g., S1, S2, SN. In an embodiment, the plant 216 is a model of the signal, which can be controlled. In an embodiment, a fusion of features is taken, to refine an apriori model for obtaining structured model. The structured model is configured to make the right inference. In an embodiment, a decision for adaptation of the model can be based on domain knowledge. The output Y is used by the control unit 214 to choose suitable inputs to the plant 216, which can control the states of the plant 216 and the corresponding system parameters. In an embodiment, the models are learned: (a) data models in terms of a dictionary, (b) topological models in terms of a graph. Further, the dictionary and the graph model is configured to precondition the raw signal (i.e. denoising and anomaly removal). In an embodiment, this approach can be applied to multiple domains and applications. In an embodiment, models represents a plurality of data models (dictionary), a plurality of structural models (graph), a plurality of measurement models and a plurality of models of the underlying states of the system (dynamical model). In another embodiment, the models can be updated/adapted with new knowledge using a model adaptation feedback. The output Y is used by the control unit 214 to choose suitable inputs to the plant 216, which can control the states of the plant (the corresponding system parameters) 216. In an embodiment, dynamics of a signal is modeled using a linear dynamical system model and deriving major Eigen modes of the system. In an embodiment, the method used is a dynamic mode decomposition. A ratio of sum of major Eigen modes to sum of minor Eigen modes is reflective of level of restructuring required in the data model for denoising and anomaly removal. The signals are non-stationary and include an energy distributed over the time-frequency plane with a certain structure. The distribution of the energy can be quantified based on an entropy measure. A time-frequency entropy measure is a measure of complexity of the signal's energy distribution over the TF plane. In an embodiment, the measure of entropy is a numerical measure to be preserved in the signal to be reconstructed from the restructured data model. Data driven representational models are derived based on one or more dictionary learning techniques. In an embodiment, a model representational accuracy and model complexity can be adapted to the requirements of preserving essential features of the signal and the statistical accuracy of the inferences which can be made on the reconstructed signal. In an embodiment, the derived data models are reconfigured to incorporate domain dependent knowledge in at least one of following steps: (a) a topological structure in terms of a graph Laplacian is utilized as a regularization penalty when refining the original data model, (b) an entropy measure based on the energy distribution is computed over the dictionary atoms of the original data model. In an embodiment, dictionary atoms with a certain entropy measure are selected. Subsequently, the reconfigured data model is configured to reconstruct the signal. The reconstruction is formulated as an optimization problem. In an embodiment, constraints on the optimization problem lead to a signal reconstruction with reduced anomalies and noise. In an embodiment, to verify whether the denoising and anomaly removal are succeeded, a dynamic mode decomposition is utilized to estimate Eigenmodes of the reconstructed signal. If the ratio of sum of major Eigenmodes to the sum of minor Eigenmodes is comparable to a domain dependent threshold, and noise, and anomalies can be considered removed. In an embodiment, a Locally linear embedding (LLE) on the reconstructed signal matrix is performed and projected to a reduced dimension space in order to verify whether the reconstructed signal preserves essential topological features of the intended signal. In an embodiment, a graph structure is learnt on the reduced dimension space. An adjacency matrix of the graph provides the topological connectivity structure of the reconstructed signal. The connectivity structure are indicative of the dominant eigenmodes of the signal. For example, the learnt graph for the reduced dimension representation includes a number of connected sub-graphs corresponding to the number of dominant eigenmodes of the signal. In an embodiment, inferences on data are based on apriori models developed by domain experts. In an embodiment, the models are defined by a structure and a set of parameters. The model structure captures the information complexity in the data. For example, a data model is defined in terms of a dictionary of atomic elements. The elements are defined in terms of dimensions of each element and cardinality (number of) of the atomic elements. In capturing structure of analog signals which are generated by complex systems of physical processes, features can be identified to capture the structure in the signal. For example features may include at least one of: (a) a distribution of the energy of the signal over the time-frequency plane, (b) a connectivity structure in terms of a graph topological structure of the signal, and combination thereof. In an embodiment, the signal model needs to be derived which can aid in making the intended inferences on the signal. An inference guided model derivation is required. The inferences can include a) estimation, b) detection, c) clustering/ classification, d) analysis/synthesis. In an exemplary scenario, in process of making inferences such as estimation, detection, clustering or classification, may require that the models of the signal are developed taking into account what critical features (statistical, spectral, structural) of the signal need to be captured. The template signal includes a certain energy distribution over the time-frequency plane and have a certain connectivity structure based on a graphical representation. For example, considering a test noisy signal, the idea is to develop a model which can aid in denoising of this signal, without distorting its essential features and corresponds to a synthesis model (i.e. aids in synthesizing a de-noised signal). An apriori model is considered, observe the data/signal and derive a posterior model which incorporates an inference relevant structure (e.g., a time-frequency property and graph property). In an embodiment, the essential features of the given test signal is preserved, while leading to the right inference (i.e. from the de-noised signal). In an embodiment, multiple models are combined to incorporate a structure on data models e.g., a data model may need to be structured for denoising/anomaly removal, by using a graph model. The model structuring is performed by combining the two models (i.e., one model acting as a penalty on the other). In an embodiment, for signal processing applications, models are derived and are reconfigured/restructured based on end goal, and constraints dependent on the domain of application. In an embodiment, the models includes methods and operators. A reconfiguration of the models are driven by at least one of a parameters, a rules and a contexts. For example, one or more rules which are predefined as mentioned below: (a) Choice rules which utilize the values of context fields, data definitions, constraints in a request, etc. to select an operator from among one or more choices available. (b) Initialization rules which formalize rough initial guesses that a specialist makes before starting a signal/data analysis task. (c) Adjustment rules are defined for operators which have adjustable parameters. Step sizes for parameters are carefully chosen hence change in behavior of the algorithm is neither too sudden nor too gradual. (d) Evaluation rules in which performance evaluation methods for algorithms and algorithm portfolios which enable an interpretation are developed. In an embodiment, algorithms are designed for signal pre-processing which use models (methods and operators) which are dependent on parameters, and can reconfigure/restructure the models based on rules and the domain context. In an embodiment, the reconfiguration and restructuring is enabled by monitoring conditions on the problem state, domain knowledge and data. The signal pre-processing system 200 is configured to reconfigure/restructure signal representational models for pre-processing of signals. The steps involved in pre-processing of signals comprises of: a. Signal Representational models: In an embodiment, a data driven approach to learning a signal subspace for analysis and synthesis of signals is the KSVD algorithm. The K-SVD approach solves the optimization problem: D_C ,X_C =?arg min ?_(D,X ) ?Y-D.X?_F + ???X??_F (1) D_C? R^(n*K) Dc is a dictionary of K orthonormal atoms learnt from the signal. Y? R^(n*P) is the matrix of raw test signal measurements to be analyzed. X_C? R^(K*P) is the matrix of synthesis coefficients which can be used to synthesize the signal. b. Structural Model: In an embodiment, to impose a structure on the synthesis model D_C, a graph topological. For example, let K measurements of n dimensions each of the distortion free i.e. template signal be represented by the matrix Y_d? R^(n*K). These multiple measurements can be characterized by a graph Laplacian L? R^(K*K). The Laplacian can be learnt by solving the optimization problem: arg ?min?_L trace (Y_d.L .Y_d^T )+ ?_1.?L .1-0?_F (2) subject to L= L^T where 1? R^(K*1) is an all ones column vector and 0? R^(K*1) is an all zeros column vector. c. Restructuring of signal representational model: 1). Global structure constraint: In an embodiment, to impose a graph structure on the dictionary, the following optimization problem is solved arg ?min?_D ?D_c-D?_F+ ?.trace (D.L .D^T) (3) The analytical solution to the above objective is given by: D=?(I+?L)?^(-1) D_C (4) In an embodiment, choice of the regularizer parameter ? is important and a larger value enforces a stronger structure. This structure enforcement increases the coherence of the dictionary D. The coherence of a dictionary is given by the maximum value of the elements of the Gram matrix: G=D^T .D In an embodiment, imposing a Laplacian penalty on the learnt dictionary atoms induces a correlation between the dictionary atoms, which is dependent on correlation between the template signal measurements on which the Graph Laplacian as learnt. In an embodiment, the choice of the penalty function determines the balance between the global structure and local structure of the signal to be synthesized. The number of dictionary atoms also plays an important role in the signal approximation. In an embodiment, an over complete dictionary with larger number of atoms than the dimensionality of the dictionary atom can better capture the correlation structure of the signal. However, an over complete dictionary tends to have a higher coherence, leading to changes in the local structure of the synthesized signal. 2). Local structure constraint: In an embodiment, an achievable balance is enforced on the data model (a global structure vs. a local structure), and are independently smoothen out the local variations introduced by the higher coherence in the data model by using a coherence reduction technique on the dictionary D_C followed by a graph Laplacian penalty on the synthesis coefficients of the newly obtained coherence reduced dictionary. In an embodiment, once coherence of the dictionary D_C is reduced, the synthesis coefficients of the coherence reduced dictionary is re-estimated by imposing a graph structural penalty on the coefficients. Considering, the coherence reduced dictionary be represented by? D?_C^R. The new Dictionary is then utilized to obtain the synthesis coefficients by solving the following optimization problem: X_1=?arg min ?_(X ) ?Y-D_C^R .X?_F+ ?_3.trace (X^T.L.X) (5) The final signal is reconstructed using: Y_1=D_C^R.X_1 (6) In an embodiment, a critical choice are made on the following design metrics: (i) The cardinality K and dimensionality n of the data model (Dictionary D_C^R), (ii) number of nodes K, in the graph which captures the correlation structure of the signal effectively, (iii) Lagrangian penalty weight on the Laplacian cost function when imposing a graph structure on the dictionary to obtain DL, (iv) The Lagrangian penalty weight on the Laplacian cost function when imposing a graph structure on the synthesis coefficients. 3. Anomaly/Outlier constraints based on signal energy distributions: There is a possibility for unexpected waveform structures to be present in the reconstructed signal even after applying the global and local structural constraints on the dictionary and corresponding synthesis coefficients by the graph Laplacian. The waveform structures include features or properties close to the valid signal structures and are not taken as outliers when solving optimization problems to reduce the noise or outliers. To address this issue, a careful selection of the dictionary atoms based on the time-frequency entropy is performed. The entropy of the distribution of the energy of the selected subset of atoms should closely approximate the distribution of a template signal (i.e., a clean signal without any anomalies or outliers). The distribution of the signal energy over the time-frequency plane is given by: W_X (t,f)= ?_t X_c (t+ t/2) X_c (t- t/2) e^(j2p f td t) (7) Xc (t) is the complex analytic form of the signal x (t). a. Signal Model reconfiguration (Dictionary atom selection): In an embodiment, a dictionary of atoms D_C^R is computed on the test signal represented by multiple measurement matrix . D^R,A=arg ?min?_(D,A) ?Y- D .A?_F+ ?.?A?_F The dictionary of atoms ?D_C^R ? R?^(n*K ) is a linear signal space. The time-frequency energy distribution of the linear signal space can be computed by averaging over all elements i.e., atoms of the dictionary. In an embodiment, a Wigner Ville distribution (WVD) of each of the k atoms is computed based on the learned dictionary atoms. This is denoted as: W_d^i (t,f) i=1, 2,..., k (8) An average WVD is computed as: W_d (t,f)= (S_i W_d^i (t,f))/K (9) In an embodiment, the averaged WVD is denoted as Wigner Ville Spectrum (WVS). In an embodiment, a normalized WVD is computed, W_d^N (t,f)= (W_d (t,f))/(?_(t,f) W_d (t,f)) (10) Further, a TF entropy of the energy distribution of the dictionary atoms is computed as: H_a (X)= 1/(1-a) log_2???_(t,f) ((W_d^N (t,f))/(?_(U,V) W_d^N (U,V) ?(24&dudv)) )^a ? (11) In an embodiment, to ensure that the WVS of the reconstructed signal is a good approximation of the WVS of the desired template signal, atoms from the dictionary are selected which include a TF Entropy less than a pre-defined threshold. In an embodiment, for a template signal (i.e., without anomalies/outliers) represented by Y_d? R^(n*K), the normalized WVS is computed, which is represented as W_y^d (t,f). The goal is to ensure that: W_y^d (t,f)˜W_d^N (t,f) (12) In an embodiment, this approach implies that the TF energy distribution of the template signal should be close (i.e., from a statistical distance perspective) to the TF energy distribution of the atoms of the dictionary. Once atoms with a specified range of TF entropy measures are selected, a new dictionary with a reduced subset of atoms is obtained and represented as D_C^E. (b) In an embodiment, reconstruction of signal is performed after selecting dictionary atoms with a TF Renyi entropy lower than a pre-defined threshold, to obtain a modified set of dictionary atoms: D_C^E. The new dictionary include plurality of dimensions ?D_C^E ? R?^(n*K1) with a number of atoms K1