Thetaray Ltd.

Other

Playtika Ltd.

Gil Shabat

David Segev

Amir Averbuch

Erez Shabat

Yaniv Shmueli

Era Choshen

Devid Segev

Nadav Carmel

Dvir Ben-Or

Accelerated machine learning using an efficient preconditioner for Kernel Ridge Regression (KRR). A plurality of anchor points may be selected by: projecting an initial kernel onto a random matrix in a lower dimensional space to generate a randomized decomposition of the initial kernel, permuting the randomized decomposition to reorder its columns and/or rows to approximate the initial kernel, and selecting anchor points representing a subset of the columns and/or rows based on their permuted order. A reduced-rank approximation kernel may be generated comprising the subset of columns and/or rows represented by the selected anchor points. A KRR system may be preconditioned using a preconditioner generated based on the reduced-rank approximation kernel. The preconditioned KRR system may be solved to train the machine learning model. This KRR technique may be executed without generating the KRR kernel, reducing processor and memory consumption.

Fast and accurate machine learning by applying efficient preconditioner to kernel ridge regression

A computer program product for performing anomaly detection, a detected anomaly being indicative of an undesirable event, the computer program product comprising: a non-transitory tangible storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for performing a method comprising: receiving data comprising a plurality m of multidimensional datapoints (MDDPs), each data point having n features; constructing a dictionary D based on the received data; embedding dictionary D into a lower dimension embedded space; and classifying, based in the lower dimension embedded space, an MDDP as an anomaly or as normal.

System and method for anomaly detection in dynamically evolving data using hybrid decomposition

Detection of abnormalities in HDBD is performed by processing it to obtain a dictionary from a training data. This is done by computing a low rank randomized LU decomposition which enables constant online updating of the training data and thus gets constant updating of the normal profile in the background.

System and method for anomaly detection in dynamically evolving data using low rank matrix decomposition

Method and system for detecting an unknown undesirable event, such as (but not limited to) a cyber-threat, a cyber-intrusion, a financial fraud event or a monitored process malfunction of breakdown. An exemplary method embodiment comprises obtaining a dataset comprising a plurality n of multidimensional data points with a dimension m&#x2265;2 wherein each data point is a vector of m features, processing the MDPs using measure-based diffusion maps to embed the MDPs into a lower dimension embedded space, and detecting in the embedded space an abnormal MDP without relying on a signature of a threat, the abnormal MDP being indicative of the unknown undesirable event.

Measure-based anomaly detection

A computer program product for performing anomaly detection, a detected anomaly being indicative of an undesirable event, the computer program product comprising a non-transitory tangible storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for performing a method comprising receiving data comprising a plurality m of multidimensional datapoints (MDDPs), each data point having n features, constructing a dictionary D based on the received data, embedding dictionary D into a reduced dimension embedded space and classifying, based in the reduced dimension embedded space, an MDDP as an anomaly or as normal.

A computer program product for performing anomaly detection, a detected anomaly being indicative of an undesirable event, the computer program product comprising a non-transitory tangible storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for performing a method comprising receiving data comprising a plurality m of multidimensional datapoints (MDDPs), each data point having n features, constructing a dictionary D based on the received data, embedding dictionary D into a lower dimension embedded space and classifying, based in the lower dimension embedded space, an MDDP as an anomaly or as normal.

Measure based anomaly detection

Method and system for detecting an unknown undesirable event, such as (but not limited to) a cyber-threat, a cyber-intrusion, a financial fraud event or a monitored process malfunction of breakdown. An exemplary method embodiments comprises obtaining a dataset comprising a plurality n of multidimensional data points with a dimension m&#x2265;2 wherein each data point is a vector of m features, processing the MDPs using measure-based diffusion maps to embed the MDPs into a lower dimension embedded space, and detecting in the embedded space an abnormal MDP without relying on a signature of a threat, the abnormal MDP being indicative of the unknown undesirable event.

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Hod Hasharon, Israel

Gil Shabat