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Large Basic Cone and Sparse Subspace Constrained Nonnegative Matrix Factorization With Kullback–Leibler Divergence for Data Representation | IEEE Journals & Magazine | IEEE Xplore

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


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 represen...Show More

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.
Published in: IEEE Intelligent Systems ( Volume: 34, Issue: 4, 01 July-Aug. 2019)
Page(s): 39 - 47
Date of Publication: 14 June 2019

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