Method and Apparatus of Processing Data Using Deep Belief Networks Employing Low-Rank Matrix Factorization

An apparatus for processing data, representing a real-world phenomenon, using an artificial neural network configured to model a real-world system or data pattern, the apparatus comprising:

• at least one processor;
• and
• at least one memory with computer code instructions stored thereon,
• the at least one processor and the at least one memory with the computer code instructions being configured to cause the apparatus to perform at least the following:
• apply a non-linear activation function to a weighted sum of input values at each node of at least one hidden layer of the artificial neural network;
• calculate a weighted sum of input values at each node of at least one low-rank layer of the artificial neural network without applying a non-linear activation function to the calculated weighted sum, the input values at each node of the at least one low-rank layer being output values from nodes of a last hidden layer of the at least one hidden layer;
• and
• generate output values by applying a non-linear activation function to a weighted sum of input values at each node of an output layer, the input values at each node of the output layer being output values from nodes of a last low-rank layer of the at least one low-rank layer of the artificial neural network.

A non-transitory computer-readable medium with computer code instructions stored thereon, the computer code instructions when executed by a processor, cause an apparatus to perform at least the following:

• applying a non-linear activation function to a weighted sum of input values at each node of at least one hidden layer of an artificial neural network;
• calculating a weighted sum of input values at each node of at least one low-rank layer of the artificial neural network without applying a non-linear activation function to the calculated weighted sum, the input values at each node of at least one low-rank layer being output values from nodes of a last hidden layer of the at least one hidden layer;
• and
• generating output values by applying a non-linear activation function to a weighted sum of input values at each node of an output layer, the input values at each node of the output layer being output values from nodes of a last low-rank layer among the at least one low-rank layer of the artificial neural network.