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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 language | English |
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Article number | 8736757 |
Pages (from-to) | 39-47 |
Number of pages | 9 |
Journal | IEEE Intelligent Systems |
Volume | 34 |
Issue number | 4 |
DOIs | |
State | Published - 1 Jul 2019 |
Keywords
- Data representation
- face recognition
- facial expression recognition
- nonnegative matrix factorization
- projected gradient descent
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Dive into the research topics of 'Large Basic Cone and Sparse Subspace Constrained Nonnegative Matrix Factorization with Kullback-Leibler Divergence for Data Representation'. Together they form a unique fingerprint.Projects
- 1 Finished
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Deep Intelligence Based Spoken Language Processing( II )
Wang, J.-C. (PI)
1/01/19 → 31/12/19
Project: Research