Anomaly Detection Using Machine Learning Models And Similarity Regularization

Abstract: Disclosed herein are embodiments for anomaly detection using machine learning models (MLMs) and similarity regularization. An embodiment operates by obtaining data for a first product, a second product, and a target product. The data include a set of sparse data points for the target product. Next, similarity scores between the target product and the first product and the second product may be calculated. The embodiment further operates by generating a target MLM associated with the target product using a regularization penalty. The regularization penalty is based on the similarity scores and distances between a target set of coefficients for the target MLM and coefficients for a first MLM and a second MLM associated with the first product and the second product, respectively. The embodiment may then detect an anomaly associated with the target product by feeding the target MLM with a feature vector associated with the target product.

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