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Multimedia Tools and Applications

, Volume 77, Issue 22, pp 29551–29572 | Cite as

Hypergraph expressing low-rank feature selection algorithm

  • Yue Fang
  • Yangding LiEmail author
  • Cong Lei
  • Yonggang Li
  • Xuelian Deng
Article
  • 239 Downloads

Abstract

Dimensionality reduction has been attracted extensive attention in machine learning. It usually includes two types: feature selection and subspace learning. Previously, many researchers have demonstrated that the dimensionality reduction is meaningful for real applications. Unfortunately, a large mass of these works utilize the feature selection and subspace learning independently. This paper explores a novel supervised feature selection algorithm by considering the subspace learning. Specifically, this paper employs an 2,1−norm and an 2,p −norm regularizers, respectively, to conduct sample denoising and feature selection via exploring the correlation structure of data. Then this paper uses two constraints (i.e. hypergraph and low-rank) to consider the local structure and the global structure among the data, respectively. Finally, this paper uses the optimizing framework to iteratively optimize each parameter while fixing the other parameter until the algorithm converges. A lot of experiments show that our new supervised feature selection method can get great results on the eighteen public data sets.

Keywords

Hypergraph LowRank Feature selection 

Notes

Acknowledgments

This work was supported in part by the China Key Research Program (Grant No: 2016YFB1000905), the China 973 Program (Grant No: 2013CB329404), the China 1000-Plan National Distinguished Professorship, the Nation Natural Science Foundation of China (Grants No: 61573270, 61672177, 61363009 and 81701780), the Guangxi Natural Science Foundation (Grant No: 2015GXNSFCB139011), 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, the Research Fund of Guangxi Key Lab of MIMS (16-A-01-01 and 16-A-01-02), and the Guangxi Bagui Teams for Innovation and Research, and Innovation Project of Guangxi Graduate Education under grant YCSW2017065, XYCSZ2017064 and XYCSZ2017067.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Yue Fang
    • 1
  • Yangding Li
    • 1
    Email author
  • Cong Lei
    • 1
  • Yonggang Li
    • 1
  • Xuelian Deng
    • 2
  1. 1.College of Computer ScienceInformation Technology Guangxi Normal UniversityGuilinPeople’s Republic of China
  2. 2.College of Public Health and ManagementGuangxi University of Chinese MedicineNanningPeople’s Republic of China

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