Sparse Nonnegative Matrix Factorization Based on Collaborative Neurodynamic Optimization

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publication9th International Conference on Information Science and Technology - ICIST2019 Final Program
PublisherIEEE
Pages114-121
ISBN (Electronic)978-1-7281-2106-2
Publication statusPublished - Aug 2019

Publication series

NameInternational Conference on Information Science and Technology

Conference

Title9th International Conference on Information Science and Technology (ICIST 2019)
PlaceChina
CityHulunbuir
Period2 - 5 August 2019

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. 

Research Area(s)

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

Citation Format(s)

Sparse Nonnegative Matrix Factorization Based on Collaborative Neurodynamic Optimization. / Che, Hangjun; Wang, Jun.

9th International Conference on Information Science and Technology - ICIST2019 Final Program. IEEE, 2019. p. 114-121 8836758 (International Conference on Information Science and Technology ).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review