Randomized latent factor model for high-dimensional and sparse matrices from industrial applications

Mingsheng Shang, Xin Luo, Zhigang Liu, Jia Chen, Ye Yuan, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review

44 Scopus citations

Abstract

Latent factor LF models are highly effective in extracting useful knowledge from High-Dimensional and Sparse HiDS matrices which are commonly seen in various industrial applications. An LF model usually adopts iterative optimizers, which may consume many iterations to achieve a local optima, resulting in considerable time cost. Hence, determining how to accelerate the training process for LF models has become a significant issue. To address this, this work proposes a randomized latent factor RLF model. It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices, thereby greatly alleviating computational burden. It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix correctly. Experimental results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models, RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data. I provides an important alternative approach to LF analysis of HiDS matrices, which is especially desired for industrial applications demanding highly efficient models.

Original languageEnglish (US)
Article number8405357
Pages (from-to)131-141
Number of pages11
JournalIEEE/CAA Journal of Automatica Sinica
Volume6
Issue number1
DOIs
StatePublished - Jan 2019

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Information Systems
  • Artificial Intelligence

Keywords

  • Big data
  • High-dimensional and sparse matrix
  • Latent factor analysis
  • Latent factor model
  • Randomized learning

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