| CPC G06N 20/20 (2019.01) [G06F 18/22 (2023.01)] | 20 Claims |
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1. A method for anomaly detection using machine learning models and similarity regularization, comprising:
storing, by at least one processor, a plurality of data points for a plurality of products comprising a first product, a second product, and a target product in a memory, wherein the plurality of data points comprises a sparse set of data points for the target product;
calculating, by the at least one processor, in response to an indication from a trained machine learning (ML) model associated with the first product that a target ML model evaluation for the target product failed to detect an anomaly for the target product, a first similarity score between the first product and the target product and a second similarity score between the second product and the target product;
calibrating, by the at least one processor and in response to the sparse set of data points, the target ML model using a regularization penalty that is based on:
the first similarity score and the second similarity score,
a first distance between a first set of coefficients for the trained ML and a target set of coefficients for the target ML model, and
a second distance between a second set of coefficients for another trained ML model associated with the second product and the target set of coefficients; and
receiving, by the at least one processor, based on feeding a feature vector associated with the target product into the target ML model, an indication that the target ML model detected the anomaly for the target product.
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