Publication Type

Journal Article

Version

publishedVersion

Publication Date

12-2022

Abstract

A high-dimensional and incomplete (HDI) matrix is a typical representation of big data. However, advanced HDI data analysis models tend to have many extra parameters. Manual tuning of these parameters, generally adopting the empirical knowledge, unavoidably leads to additional overhead. Although variable adaptive mechanisms have been proposed, they cannot balance the exploration and exploitation with early convergence. Moreover, learning such multi-parameters brings high computational time, thereby suffering gross accuracy especially when solving a bilinear problem like conducting the commonly used latent factor analysis (LFA) on an HDI matrix. Herein, an efficient annealing-assisted differential evolution for multi-parameter adaptive latent factor analysis (ADMA) is proposed to address these problems. First, a periodic equilibrium mechanism is employed using the physical mechanism annealing, which is embedded in the mutation operation of differential evolution (DE). Then, to further improve its efficiency, we adopt a probabilistic evaluation mechanism consistent with the crossover probability of DE. Experimental results of both adaptive and non-adaptive state-of-the-art methods on industrial HDI datasets illustrate that ADMA achieves a desirable global optimum with reasonable overhead and prevails competing methods in terms of predicting the missing data in HDI matrices.

Keywords

Big data analysis, Latent factor analysis, Simulated annealing, Differential evolution algorithm, Multi-parameter adaptive

Discipline

Numerical Analysis and Scientific Computing | Theory and Algorithms

Research Areas

Intelligent Systems and Optimization

Publication

Journal Of Big Data

Volume

9

Issue

1

First Page

1

Last Page

18

ISSN

2196-1115

Identifier

10.1186/s40537-022-00638-8

Publisher

SpringerOpen

Copyright Owner and License

Authors CC-BY

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Additional URL

https://doi.org/10.1186/s40537-022-00638-8

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