US 11,886,994 B1
System and method for anomaly detection in dynamically evolving data using random neural network decomposition
David Segev, Lapid (IL)
Assigned to ThetaRay Lid., Hod HaSharon (IL)
Filed by ThetaRay Ltd., Hod HaSharon (IL)
Filed on Jun. 11, 2021, as Appl. No. 17/345,230.
Application 17/345,230 is a continuation of application No. 15/348,996, filed on Nov. 11, 2016, granted, now 11,049,004.
Claims priority of provisional application 62/255,480, filed on Nov. 15, 2015.
Int. Cl. G06N 3/08 (2023.01); G06F 11/07 (2006.01)
CPC G06N 3/08 (2013.01) [G06F 11/0721 (2013.01); G06F 11/0751 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A 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:
a) receiving input data in the form of a matrix A of size m×n and rank k, wherein m represents a plurality of multidimensional data points (MDDPs) and wherein n represents a dimension of each MDDP;
b) constructing iteratively i dictionaries Di, wherein each Di is a matrix Bi=Ai′Ri of dimension ki×n, wherein Ai′ is the transpose of matrix Ai, wherein ki is a rank of matrix Ai, wherein Ri is a Gaussian distributed random matrix of dimension mi×ki, wherein each mi is smaller than an iteration mi−1, wherein mi−1 is an immediately preceding iteration, and wherein each Di is constructed by applying to Ai a multi-layer feedforward artificial neural network made of one hidden layer (NN(Ai)) such that ∥Bi−NN(Ai)∥ is minimized with respect to parameters of the neural network;
c) concatenating all dictionaries Di to construct a dictionary D; and
d) using dictionary D to classify a MDDP as an anomaly or as normal, wherein a detected anomaly is indicative of an undesirable event,
whereby a reduction in m enhances the performance of a computer including the computer program product in both processing and storage terms.