Sparse Nonnegative Matrix Factorization Based on Collaborative Neurodynamic Optimization

Hangjun Che, Jun Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

This paper presents a collaborative neurodynamic approach to sparse nonnegative matrix factorization (SNMF). SNMF is formulated as a bilevel optimization problem. In the lower level of the problem, the sparsity of factorized matrix is minimized subject to the factorization error and nonnegative constraints. In the upper level of the problem, the parameter of the inverted Gaussian function is minimized to approximate l0 norm. A group of neurodynamic models operating at two timescales is employed to solve the reformulated problem. The experimental results show the superiority of the proposed approach.

Original languageEnglish
Title of host publication9th International Conference on Information Science and Technology, ICIST 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages114-121
Number of pages8
ISBN (Electronic)9781728121062
DOIs
Publication statusPublished - Aug 2019
Externally publishedYes
Event9th International Conference on Information Science and Technology, ICIST 2019 - Hulunbuir, China
Duration: 2 Aug 20195 Aug 2019

Publication series

Name9th International Conference on Information Science and Technology, ICIST 2019

Conference

Conference9th International Conference on Information Science and Technology, ICIST 2019
Country/TerritoryChina
CityHulunbuir
Period2/08/195/08/19

Keywords

  • Bilevel optimization
  • Collaborative neurodynamic approach.
  • Sparse nonnegative matrix factorization

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