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Joint Subspace Learning and Sparse Regression for Feature Selection in Kernel Space

  • Long Chen
  • Zhi ZhongEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 950)

Abstract

In this paper, we propose a novel feature selection method to jointly map original data to kernel space and conduct both subspace learning (via locality preserving projection) and feature selection (via a sparsity constraint). The kernel method is used to explore the nonlinear relationship between data and subspace learning is used to maintain the local structure of the data. As a result, we eliminate redundant and irrelevant features and thus make our method select a large amount of informative and distinguishing features. By comparing our proposed method with some state-of-the-art methods, the experimental results showed that the proposed method outperformed the comparisons in terms of clustering task.

Keywords

Feature selection Kernel method Subspace learning Sparse learning Locality preserving projection 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.College of Computer and Information EngineeringGuangxi Teachers Education UniversityNanningChina
  2. 2.College of Continue EducationGuangxi Teachers Education UniversityNanningChina

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