Large Basic Cone and Sparse Subspace Constrained Nonnegative Matrix Factorization With Kullback–Leibler Divergence for Data Representation

摘要

In this article, a new constrained NMF model with Kullback-Leibler (KL) divergence is developed for data representation. It is called large basic cone and sparse representation-constrained nonnegative matrix factorization with Kullback-Leibler divergence (conespaNMF_KL). It achieves sparseness from a large simplicial cone constraint on the base and sparse regularize on the extracted features.