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Volume 8 Issue 4
Apr.  2021

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2021  >  8(4): 796-805
Di Wu, Xin Luo, "Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data," IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 796-805, Apr. 2021. doi: 10.1109/JAS.2020.1003533
Citation: Di Wu, Xin Luo, "Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data," IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 796-805, Apr. 2021. doi: 10.1109/JAS.2020.1003533
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Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data

doi: 10.1109/JAS.2020.1003533
  • 1.

    Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China

  • 2.

    Chongqing School, University of Chinese Academy of Sciences, Chongqing 400714, China

Funds:  This work was supported in part by the National Natural Science Foundation of China (61702475, 61772493, 61902370, 62002337), in part by the Natural Science Foundation of Chongqing, China (cstc2019jcyj-msxmX0578, cstc2019jcyjjqX0013), in part by the Chinese Academy of Sciences “Light of West China” Program, in part by the Pioneer Hundred Talents Program of Chinese Academy of Sciences, and by Technology Innovation and Application Development Project of Chongqing, China (cstc2019jscx-fxydX0027)
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    Abstract
  • High-dimensional and sparse (HiDS) matrices commonly arise in various industrial applications, e.g., recommender systems (RSs), social networks, and wireless sensor networks. Since they contain rich information, how to accurately represent them is of great significance. A latent factor (LF) model is one of the most popular and successful ways to address this issue. Current LF models mostly adopt L 2-norm-oriented Loss to represent an HiDS matrix, i.e., they sum the errors between observed data and predicted ones with L 2-norm. Yet L 2-norm is sensitive to outlier data. Unfortunately, outlier data usually exist in such matrices. For example, an HiDS matrix from RSs commonly contains many outlier ratings due to some heedless/malicious users. To address this issue, this work proposes a smooth L 1-norm-oriented latent factor (SL-LF) model. Its main idea is to adopt smooth L 1-norm rather than L 2-norm to form its Loss, making it have both strong robustness and high accuracy in predicting the missing data of an HiDS matrix. Experimental results on eight HiDS matrices generated by industrial applications verify that the proposed SL-LF model not only is robust to the outlier data but also has significantly higher prediction accuracy than state-of-the-art models when they are used to predict the missing data of HiDS matrices.

     

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    Highlights

    • Proposing an SL-LF model with high robustness and accuracy in predicting the missing data of an HiDS matrix.
    • Performing a suite of theoretical analyses and algorithm designs for the proposed SL-LF model.
    • Conducting extensive empirical studies on eight HiDS matrices generated by industrial applications to evaluate the proposed model and other state-of-the-art ones.

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