Dictionary Learning Based on Nonnegative Matrix Factorization Using Parallel Coordinate Descent
Joint Authors
Tang, Zunyi
Li, Zhenni
Jiang, Linlin
Ding, Shuxue
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-06-04
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Tang, Zunyi& Ding, Shuxue& Li, Zhenni& Jiang, Linlin. 2013. Dictionary Learning Based on Nonnegative Matrix Factorization Using Parallel Coordinate Descent. Abstract and Applied Analysis،Vol. 2013, no. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-458233
Modern Language Association (MLA)
Tang, Zunyi…[et al.]. Dictionary Learning Based on Nonnegative Matrix Factorization Using Parallel Coordinate Descent. Abstract and Applied Analysis No. 2013 (2013), pp.1-11.
https://search.emarefa.net/detail/BIM-458233
American Medical Association (AMA)
Tang, Zunyi& Ding, Shuxue& Li, Zhenni& Jiang, Linlin. Dictionary Learning Based on Nonnegative Matrix Factorization Using Parallel Coordinate Descent. Abstract and Applied Analysis. 2013. Vol. 2013, no. 2013, pp.1-11.
https://search.emarefa.net/detail/BIM-458233
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-458233