A Collaborative Neurodynamic Approach to Symmetric Nonnegative Matrix Factorization

Hangjun Che; Jun Wang

doi:10.1007/978-3-030-04179-3_40

A Collaborative Neurodynamic Approach to Symmetric Nonnegative Matrix Factorization

Hangjun Che^*
, Jun Wang

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

4 Citations (Scopus)

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.

Original language	English
Title of host publication	Neural Information Processing
Subtitle of host publication	Proceedings, Part II
Editors	Long Cheng, Andrew Chi Sing Leung, Seiichi Ozawa
Publisher	Springer Nature Switzerland AG
Pages	453-462
ISBN (Electronic)	9783030041793
ISBN (Print)	9783030041786
DOIs	https://doi.org/10.1007/978-3-030-04179-3_40
Publication status	Published - Dec 2018
Event	25th International Conference on Neural Information Processing (ICONIP 2018) - Sokha Siem Reap Resort & Convention Center, Siem Reap, Cambodia Duration: 13 Dec 2018 → 16 Dec 2018 https://conference.cs.cityu.edu.hk/iconip/

Publication series

Name	Lecture Notes in Computer Science
Volume	LNCS 11302
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	25th International Conference on Neural Information Processing (ICONIP 2018)
Abbreviated title	ICONIP 2018
Place	Cambodia
City	Siem Reap
Period	13/12/18 → 16/12/18
Internet address	https://conference.cs.cityu.edu.hk/iconip/

Research Keywords

Augmented Lagrangian function
Collaborative neurodynamic approach
Symmetric nonnegative matrix factorization

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10.1007/978-3-030-04179-3_40

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@inproceedings{9d3b8995a90e4723a276f9d5704d6f17,

title = "A Collaborative Neurodynamic Approach to Symmetric Nonnegative Matrix Factorization",

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.",

keywords = "Augmented Lagrangian function, Collaborative neurodynamic approach, Symmetric nonnegative matrix factorization",

author = "Hangjun Che and Jun Wang",

year = "2018",

month = dec,

doi = "10.1007/978-3-030-04179-3\_40",

language = "English",

isbn = "9783030041786",

series = "Lecture Notes in Computer Science",

publisher = "Springer Nature Switzerland AG",

pages = "453--462",

editor = "Long Cheng and Leung, \{Andrew Chi Sing\} and Seiichi Ozawa",

booktitle = "Neural Information Processing",

address = "Switzerland",

note = "25th International Conference on Neural Information Processing (ICONIP 2018), ICONIP 2018 ; Conference date: 13-12-2018 Through 16-12-2018",

url = "https://conference.cs.cityu.edu.hk/iconip/",

}

Che, H & Wang, J 2018, A Collaborative Neurodynamic Approach to Symmetric Nonnegative Matrix Factorization. in L Cheng, ACS Leung & S Ozawa (eds), Neural Information Processing: Proceedings, Part II. Lecture Notes in Computer Science, vol. LNCS 11302 , Springer Nature Switzerland AG, pp. 453-462, 25th International Conference on Neural Information Processing (ICONIP 2018), Siem Reap, Cambodia, 13/12/18. https://doi.org/10.1007/978-3-030-04179-3_40

A Collaborative Neurodynamic Approach to Symmetric Nonnegative Matrix Factorization. / Che, Hangjun ; Wang, Jun.
Neural Information Processing: Proceedings, Part II. ed. / Long Cheng; Andrew Chi Sing Leung; Seiichi Ozawa. Springer Nature Switzerland AG, 2018. p. 453-462 (Lecture Notes in Computer Science; Vol. LNCS 11302 ).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A Collaborative Neurodynamic Approach to Symmetric Nonnegative Matrix Factorization

AU - Che, Hangjun

AU - Wang, Jun

PY - 2018/12

Y1 - 2018/12

N2 - 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.

AB - 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.

KW - Augmented Lagrangian function

KW - Collaborative neurodynamic approach

KW - Symmetric nonnegative matrix factorization

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DO - 10.1007/978-3-030-04179-3_40

M3 - RGC 32 - Refereed conference paper (with host publication)

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T3 - Lecture Notes in Computer Science

SP - 453

EP - 462

BT - Neural Information Processing

A2 - Cheng, Long

A2 - Leung, Andrew Chi Sing

A2 - Ozawa, Seiichi

PB - Springer Nature Switzerland AG

T2 - 25th International Conference on Neural Information Processing (ICONIP 2018)

Y2 - 13 December 2018 through 16 December 2018

ER -

A Collaborative Neurodynamic Approach to Symmetric Nonnegative Matrix Factorization

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