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	Randomized latent factor model for high-dimensional and sparse matrices from industrial applications
	Chen, Jia1; Luo, Xin2
	2018
摘要	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, how to accelerate the training process of an LF model becomes a highly significant issue. To address it, this work innovatively proposes a randomized latent factor (RLF) model. It incorporates the principle of randomized learning techniques for neural networks into the LF analysis on HiDS matrices to alleviate the computational burden greatly. It also extends the 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. More importantly, it provides a novel, effective, and efficient approach to LF analysis on HiDS matrices. © 2018 IEEE.
语种	英语
DOI	10.1109/ICNSC.2018.8361355
会议(录)名称	15th IEEE International Conference on Networking, Sensing and Control, ICNSC 2018
页码	1-7
收录类别	EI
会议地点	Zhuhai, China
会议日期	March 27, 2018 - March 29, 2018