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Multi-view feature selection via sparse tensor regression

Corrections for this article
- Erratum: Multi-view feature selection via sparse tensor regression

State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macao, P. R. China

E-mail Address: hlyuan@must.edu.mo

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Sio-Long Lo

Faculty of Information Technology, Macau University of Science and Technology, Macao, P. R. China

E-mail Address: sllo@must.edu.mo

Corresponding author.

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Ming Yin

School of Automation, Guangdong University of Technology, Guangzhou, P. R. China

E-mail Address: yiming@gdut.edu.cn

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, and

Yong Liang

State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macao, P. R. China

Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology, Foshan University, Foshan 528225, China

E-mail Address: yliang@must.edu.mo

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https://doi.org/10.1142/S021969132150020XCited by:1 (Source: Crossref)

Abstract

In this paper, we propose a sparse tensor regression model for multi-view feature selection. Apart from the most of existing methods, our model adopts a tensor structure to represent multi-view data, which aims to explore their underlying high-order correlations. Based on this tensor structure, our model can effectively select the meaningful feature set for each view. We also develop an iterative optimization algorithm to solve our model, together with analysis about the convergence and computational complexity. Experimental results on several popular multi-view data sets confirm the effectiveness of our model.

Keywords:

AMSC: 68U10, 62H35

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