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

Viet Hang Duong, Manh Quan Bui, Yung Hui Li, Jia Ching Wang, Tzu Chiang Tai

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Article number8736757
Pages (from-to)39-47
Number of pages9
JournalIEEE Intelligent Systems
Volume34
Issue number4
DOIs
StatePublished - 1 Jul 2019

Keywords

  • Data representation
  • face recognition
  • facial expression recognition
  • nonnegative matrix factorization
  • projected gradient descent

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