A Collaborative Neurodynamic Approach to Symmetric Nonnegative Matrix Factorization
Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with ISBN/ISSN) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| 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 |
| Publication status | Published - Dec 2018 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Volume | LNCS 11302 |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Title | 25th International Conference on Neural Information Processing (ICONIP 2018) |
|---|---|
| Location | Sokha Siem Reap Resort & Convention Center |
| Place | Cambodia |
| City | Siem Reap |
| Period | 13 - 16 December 2018 |
Link(s)
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.
Research Area(s)
- Augmented Lagrangian function, Collaborative neurodynamic approach, Symmetric nonnegative matrix factorization
Citation Format(s)
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: 12, 32, 41, 45) › 32_Refereed conference paper (with ISBN/ISSN) › peer-review
