SYSTEM AND METHOD FOR LOW-RANK MATRIX FACTORIZATION FOR DEEP BELIEF NETWORK TRAINING WITH HIGH-DIMENSIONAL OUTPUT TARGETS

摘要

Systems and methods for reducing a number of training parameters in a deep belief network (DBN) are provided. A method for reducing a number of training parameters in a deep belief network (DBN) comprises determining a network architecture including a plurality of layers, using matrix factorization to represent a weight matrix of a final layer of the plurality of layers as a plurality of matrices, and training the DBN having the plurality of matrices.