Accelerated Non-negative Latent Factor Analysis on High-Dimensional and Sparse Matrices via Generalized Momentum Method

Zhigang Liu, Xin Luo, Shuai Li, Mingsheng Shang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Non-negative latent factor (NLF) models can efficiently acquire useful knowledge from high-dimensional and sparse (HiDS) matrices filled with non-negative data. Single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) is an efficient algorithm for building an NLF model on an HiDS matrix, yet it suffers slow convergence. On the other hand, a momentum method is frequently adopted to accelerate a learning algorithm explicitly depending on gradients, yet it is incompatible with learning algorithms implicitly depending on gradients, like SLF-NMU. To build a fast NLF model, we firstly propose a generalized momentum method compatible with SLF-NMU. With it, we propose the single latent factor-dependent, non-negative, multiplicative and momentum-integrated update (SLF-NM2U) algorithm for accelerating the building process of an NLF model, thereby achieving a fast non-negative latent factor (FNLF) model. Empirical studies on six HiDS matrices from industrial application indicate that with the incorporated momentum effects, FNLF outperforms NLF in terms of both convergence rate and prediction accuracy for missing data. Hence, compare with an NLF model, an FNLF model is more practical in industrial applications.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3051-3056
Number of pages6
ISBN (Electronic)9781538666500
DOIs
Publication statusPublished - 16 Jan 2019
Event2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japan
Duration: 7 Oct 201810 Oct 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

Conference

Conference2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Country/TerritoryJapan
CityMiyazaki
Period7/10/1810/10/18

Keywords

  • Big-data
  • High-Dimensional and Sparse Matrix
  • Latent Factor Analysis
  • Missing data Estimation
  • Non-negative Latent Factor Model

ASJC Scopus subject areas

  • Information Systems
  • Information Systems and Management
  • Health Informatics
  • Artificial Intelligence
  • Computer Networks and Communications
  • Human-Computer Interaction

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