scholarly journals A Fast Non-Smooth Nonnegative Matrix Factorization for Learning Sparse Representation

IEEE Access ◽  
2016 ◽  
Vol 4 ◽  
pp. 5161-5168 ◽  
Author(s):  
Zuyuan Yang ◽  
Yu Zhang ◽  
Wei Yan ◽  
Yong Xiang ◽  
Shengli Xie
Keyword(s):  
Sparse Representation ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix

scholarly journals Dictionary Learning Based on Nonnegative Matrix Factorization Using Parallel Coordinate Descent

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Zunyi Tang ◽  
Shuxue Ding ◽  
Zhenni Li ◽  
Linlin Jiang
Keyword(s):  
Sparse Representation ◽  
Dictionary Learning ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Coordinate Descent ◽  
Multispectral Data ◽  
Overcomplete Dictionary ◽  
Novel Method ◽  
Better Than

Sparse representation of signals via an overcomplete dictionary has recently received much attention as it has produced promising results in various applications. Since the nonnegativities of the signals and the dictionary are required in some applications, for example, multispectral data analysis, the conventional dictionary learning methods imposed simply with nonnegativity may become inapplicable. In this paper, we propose a novel method for learning a nonnegative, overcomplete dictionary for such a case. This is accomplished by posing the sparse representation of nonnegative signals as a problem of nonnegative matrix factorization (NMF) with a sparsity constraint. By employing the coordinate descent strategy for optimization and extending it to multivariable case for processing in parallel, we develop a so-called parallel coordinate descent dictionary learning (PCDDL) algorithm, which is structured by iteratively solving the two optimal problems, the learning process of the dictionary and the estimating process of the coefficients for constructing the signals. Numerical experiments demonstrate that the proposed algorithm performs better than the conventional nonnegative K-SVD (NN-KSVD) algorithm and several other algorithms for comparison. What is more, its computational consumption is remarkably lower than that of the compared algorithms.

scholarly journals Improved hypergraph regularized Nonnegative Matrix Factorization with sparse representation

2018 ◽  
Vol 102 ◽  
pp. 8-14 ◽  
Author(s):  
Sheng Huang ◽  
Hongxing Wang ◽  
Yongxin Ge ◽  
Luwen Huangfu ◽  
Xiaohong Zhang ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix
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