Nonlinear non-negative matrix factorization using deep learning

Abstract

In this paper, we describe the deep learning method to reduce the dimension of the data samples under the framework Non-negative Matrix Factorization (NMF). That is to say, we try to find the good representation of the data samples for the task of NMF. To this end, a nonlinear NMF optimization model is constructed and the optimization algorithm is developed. The experimental results on some benchmark dataset show the nonlinear dimension reduction helps the NMF to improve the clustering performance.