Deep semi-nonnegative matrix factorization with elastic preserving for data representation

Deep matrix factorization methods can automatically learn the hidden representation of high dimensional data. However, they neglect the intrinsic geometric structure information of data. In this paper, we propose a Deep Semi-Nonnegative Matrix Factorization with Elastic Preserving (Deep Semi-NMF-EP)...

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Bibliographic Details
Published inMultimedia tools and applications Vol. 80; no. 2; pp. 1707 - 1724
Main Authors Shu, Zhen-qiu, Wu, Xiao-jun, Hu, Cong, You, Cong-zhe, Fan, Hong-hui
Format Journal Article
LanguageEnglish
Published New York Springer US 2021
Springer Nature B.V
Subjects
Algorithms
Clustering
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Factorization
Methods
Multimedia
Multimedia Information Systems
Optimization algorithms
Pattern recognition
Representations
Special Purpose and Application-Based Systems
Deep matrix factorization
Geometric structure
Elasticity
High dimensional data
Clustering
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Summary:Deep matrix factorization methods can automatically learn the hidden representation of high dimensional data. However, they neglect the intrinsic geometric structure information of data. In this paper, we propose a Deep Semi-Nonnegative Matrix Factorization with Elastic Preserving (Deep Semi-NMF-EP) method by adding two graph regularizers in each layer. Therefore, the proposed Deep Semi-NMF-EP method effectively preserves the elasticity of data and thus can learn a better representation of high-dimensional data. In addition, we present an effective algorithm to optimize the proposed model and then provide its complexity analysis. The experimental results on the benchmark datasets show the excellent performance of our proposed method compared with other state-of-the-art methods.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-020-09766-w