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Low-rank feature selection for multi-view regression

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Abstract

Real life data and information often have different ways to obtain. For example, in computer vision, we can describe an objective by different types, such as text, video and picture. And even from variety of angles. These different descriptors of the same object are usually called multi-view data. In ordinarily, dimensional reduction methods usually include feature selection and subspace learning, respectively, can have better interpretative capability and stabilizing performance, and now are very prevalent method for high-dimensional data. However, it is usually not considering the relationship among class indicators, so the performance of regression model is not very ideal. In this paper, we simultaneously consider feature selection, low-rank selection, and subspace learning into a unified framework. Specifically, under the framework of linear regression model, we first use the low-rank constraint to feature selection which considers two aspects of information inherent in data. The low-rank constraint takes the correlation of response variables into account, then embed an ℓ 2, p -norm regularizer to consider the correlation among variety of class indicators, and feature vectors and their corresponding response variables. Meanwhile, we take LDA algorithm which belong to the subspace learning to further adjust relevant feature selection results into account. Lastly, we conducted experiments on several real multi-views image sets and corresponding experimental consequences also validated the furnished method outperformed all comparison algorithms.

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Notes

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

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Acknowledgments

This work was supported in part by the China “1000-Plan” National Distinguished Professorship; the Nation Natural Science Foundation of China (Grant No: 61263035, 61363009, 61573270 and 61672177), the China 973 Program (Grant No: 2013CB329404); the China Key Research Program (Grant No: 2016YFB1000905); the Guangxi Natural Science Foundation (Grant No: 2012GXNSFGA060004 and 2015GXNSFCB139011); the China Postdoctoral Science Foundation (Grant No: 2015M570837); the Innovation Project of Guangxi Graduate Education under grant YCSZ2016046; the Guangxi High Institutions’ Program of Introducing 100 High-Level Overseas Talents; the Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing; and the Guangxi “Bagui” Teams for Innovation and Research.

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Authors and Affiliations

  1. Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, Guangxi, 541004, People’s Republic of China

    Rongyao Hu, Debo Cheng, Wei He, Guoqiu Wen & Shichao Zhang

  2. Guangxi University, Nanning, Guangxi, 530004, People’s Republic of China

    Yonghua Zhu

  3. Guangxi University of Finance and Economics, Nanning, Guangxi, 530003, People’s Republic of China

    Jilian Zhang

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  1. Rongyao Hu
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  2. Debo Cheng
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Correspondence to Shichao Zhang.

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Hu, R., Cheng, D., He, W. et al. Low-rank feature selection for multi-view regression. Multimed Tools Appl 76, 17479–17495 (2017). https://doi.org/10.1007/s11042-016-4119-2

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  • DOI: https://doi.org/10.1007/s11042-016-4119-2

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