Accurate Latent Factor Analysis via Particle Swarm Optimizers

Jia Chen, Xin Luo, Mengchu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A stochastic-gradient-descent-based Latent Factor Analysis (LFA) model is highly efficient in representative learning of a High-Dimensional and Sparse (HiDS) matrix. Its learning rate adaptation is vital in ensuring its efficiency. Such adaptation can be realized with an evolutionary computing algorithm. However, a resultant model tends to suffer from two issues: a) the pre-mature convergence of the swarm of learning rates as caused by an adopted evolution algorithm, and b) the pre-mature convergence of the LFA model as caused jointly by evolution-based learning rate adaptation and an optimization algorithm. This paper focuses on the methods to address such issues. A Hierarchical Particle-swarm-optimization-incorporated Latent factor analysis (HPL) model with a two-layered structure is proposed, where the first layer pre-trains desired latent factors with a position-transitional particle-swarm-optimization-based LFA model, and the second layer performs latent factor refining with a newly-proposed mini-batch particle swarm optimizer. With such design, an HPL model can well handle the pre-mature convergence, which is supported by the positive experimental results achieved on HiDS matrices from industrial applications.

Original languageEnglish (US)
Title of host publication2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2930-2935
Number of pages6
ISBN (Electronic)9781665442077
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021 - Melbourne, Australia
Duration: Oct 17 2021Oct 20 2021

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)1062-922X

Conference

Conference2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
Country/TerritoryAustralia
CityMelbourne
Period10/17/2110/20/21

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

Keywords

  • Big Data
  • High-dimensional and Sparse Matrix
  • Industrial Application
  • Large-Scale Incomplete Data
  • Latent Factor Analysis (LFA)
  • Machine Learning
  • Missing Data Estimation
  • Particle Swarm Optimization (PSO)

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