A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

Document Type

Article

Publication Date

1-1-2024

Abstract

High-dimensional and incomplete (HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis (LFA) model is capable of conducting efficient representation learning to an HDI matrix, whose hyper-parameter adaptation can be implemented through a particle swarm optimizer (PSO) to meet scalable requirements. However, conventional PSO is limited by its premature issues, which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer (SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational efficiency. Hence, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

Identifier

85207289379 (Scopus)

Publication Title

IEEE/CAA Journal of Automatica Sinica

External Full Text Location

https://doi.org/10.1109/JAS.2024.124575

e-ISSN

23299274

ISSN

23299266

First Page

2220

Last Page

2235

Issue

11

Volume

11

Grant

CSTB2023NSCQ-LZX0069

Fund Ref

Natural Science Foundation of Chongqing Municipality

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