Sparse Nonnegative Matrix Factorization Based on a Hyperbolic Tangent Approximation of L0-Norm and Neurodynamic Optimization

Xinqi LI, Jun WANG, Sam KWONG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

6 Citations (Scopus)

Abstract

Sparse nonnegative matrix factorization (SNMF) attracts much attention in the past two decades because its sparse and part-based representations are desirable in many machine learning applications. Due to the combinatorial nature of the sparsity constraint in form of l0-norm, the problem is hard to solve. In this paper, a hyperbolic tangent function is introduced to approximate the l0-norm. A discrete-time neurodynamic approach is developed for solving the proposed formulation. The stability and the convergence behavior are shown for the state vectors. Experiment results are discussed to demonstrate the superiority of the approach. The results show that this approach outperforms other sparse NMF approaches with the smallest relative reconstruction error and the required level of sparsity.
Original languageEnglish
Title of host publicationTwelfth International Conference on Advanced Computational Intelligence
PublisherIEEE
Pages542-549
Number of pages8
ISBN (Electronic)9781728142487
ISBN (Print)9781728142494
DOIs
Publication statusPublished - Aug 2020
Externally publishedYes
Event12th International Conference on Advanced Computational Intelligence (ICACI 2020) - Yunnan, China
Duration: 14 Aug 202016 Aug 2020

Conference

Conference12th International Conference on Advanced Computational Intelligence (ICACI 2020)
Country/TerritoryChina
CityYunnan
Period14/08/2016/08/20

Bibliographical note

This work was supported in part by the Research Grants Council of the Hong Kong Special Administrative Region of China under Grants 11200116, 11206317, 11208517, 11202318, 11202019, and 11209819, and by the National Natural Science Foundation of China under Grant 61673330 and 61876105; and by International Partnership Program of Chinese Academy of Sciences under Grant GJHZ1849; and by the Key Project of Science and Technology Innovation 2030 supported by the Ministry of Science and Technology of China (Grants No. 2018AAA0100300 and No. 2018AAA0101301).

Keywords

  • Neurodynamic optimization
  • Sparse nonnegative matrix factorization

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