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Low-rank unsupervised graph feature selection via feature self-representation

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Abstract

Feature selection and subspace learning are two popular approaches of dimensionality reduction for solving the issue of ‘curse of dimensionality’ in high-dimensional data. However, most of previous methods of feature selection and subspace learning ignore the fact that there exist noise and outliers in high-dimensional data, which increase the rank of the data matrix so that decreasing the stability of learning models. In this paper, we integrate a feature-level self-representation loss function, a low-rank constraint, a graph Laplacian regularizer, and a sparsity regularizer into a unified framework to conduct unsupervised feature selection for solving mentioned issues. Specifically, we first propose a new feature-level self-representation loss function plus a sparsity regularizer (ℓ 2,1-norm regularizer) to select representative features, and then push a low-rank constraint on the coefficient matrix which considers the response variables as a whole group to avoid the impact of noise and outliers, and a graph regularizer to preserve the local structures of the data to conduct subspace learning in the framework of feature selection. Experimental results on real databases implied that the proposed method effectively selected the most representative features and removed the adverse effect of irrelevant features, compared to the state-of-the-art methods.

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Notes

  1. Available at http://www.csie.ntu.edu.tw/~cjlin/libsvm/

  2. Available at http://archive.ics.uci.edu/ml/

  3. Available at http://featureselection.asu.edu/datasets.php

  4. Available at http://see.xidian.edu.cn/vipsl/database_Face.html

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (Grant No: 61263035, 61573270, 61450001 and 61363009), the China 973 Program (Grant No: 2013CB329404), the Guangxi Natural Science Foundation (Grant No: 2015GXNSFCB139011), the Guangxi Higher Institutions’ Program of Introducing 100 High-Level Over-seas Talents, the Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing, Innovation Project of Guangxi Graduate Education under grant YCSZ2016046 and the project “Application and Research of Big Data Fusion in Inter-City Traffic Integration of The Xijiang River - Pearl River Economic Belt(da shu jv rong he zai xijiang zhujiang jing ji dai cheng ji jiao tong yi ti hua zhong de ying yong yu yan jiu )”.

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Authors and Affiliations

  1. Guangxi Key Laboratory of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, 541004, China

    Wei He, Xiaofeng Zhu, Debo Cheng, Rongyao Hu & Shichao Zhang

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  1. Wei He
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  2. Xiaofeng Zhu
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  3. Debo Cheng
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  4. Rongyao Hu
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  5. Shichao Zhang
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Correspondence to Xiaofeng Zhu or Shichao Zhang.

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He, W., Zhu, X., Cheng, D. et al. Low-rank unsupervised graph feature selection via feature self-representation. Multimed Tools Appl 76, 12149–12164 (2017). https://doi.org/10.1007/s11042-016-3937-6

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  • DOI: https://doi.org/10.1007/s11042-016-3937-6

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