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International Conference on Neural Information Processing

ICONIP 2018: Neural Information Processing pp 453–462Cite as

A Collaborative Neurodynamic Approach to Symmetric Nonnegative Matrix Factorization

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11302))

Abstract

This paper presents a collaborative neurodynamic approach to symmetric nonnegative matrix factorization (SNMF). First, a formulated nonconvex optimization problem of SNMF is described. To solve this problem, a neurodynamic model based on an augmented Lagrangian function is proposed and proven to be convergent to a strict local optimal solution under the second-order sufficiency condition. Next, a group of neurodynamic models are employed to search for an optimal factorized matrix by using particle swarm algorithm to update the initial neuronal states. The efficacy of the proposed approach is substantiated on two datasets.

This work was supported in part by the Research Grants Council of the Hong Kong Special Administrative Region of China, under Grants 14207614 and 11208517, and in part by the National Natural Science Foundation of China under grant 61673330.

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Author information

Authors and Affiliations

  1. Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong

    Hangjun Che & Jun Wang

  2. Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China

    Hangjun Che & Jun Wang

Authors
  1. Hangjun Che
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  2. Jun Wang
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Corresponding authors

Correspondence to Hangjun Che or Jun Wang .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Che, H., Wang, J. (2018). A Collaborative Neurodynamic Approach to Symmetric Nonnegative Matrix Factorization. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11302. Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_40

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  • DOI: https://doi.org/10.1007/978-3-030-04179-3_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04178-6

  • Online ISBN: 978-3-030-04179-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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