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 language | English (US) |
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Article number | 8405357 |
Pages (from-to) | 131-141 |
Number of pages | 11 |
Journal | IEEE/CAA Journal of Automatica Sinica |
Volume | 6 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2019 |
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Information Systems
- Control and Optimization
- Artificial Intelligence
Keywords
- Big data
- High-dimensional and sparse matrix
- Latent factor analysis
- Latent factor model
- Randomized learning