Unsupervised feature selection based on self-representation sparse regression and local similarity preserving View Full Text


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Article Info

DATE

2019-04

AUTHORS

Ronghua Shang, Jiangwei Chang, Licheng Jiao, Yu Xue

ABSTRACT

Feature selection, as an indispensable method of data preprocessing, has attracted the attention of researchers. In this paper, we propose a new feature selection model called unsupervised feature selection based on self-representation sparse regression and local similarity preserving, i.e., UFSRL. Specifically, UFSRL is sparse reconstruction of the original data itself, rather than fitting low-dimensional embedding, and the manifold learning exerted on UFSRL model to preserve the local similarity of the data. Moreover, the l2,1/2-matrix norm has been imposed on the coefficient matrix, which make the proposed model sparse and robust to noise. In order to solve the proposed model, we design an effective iterative algorithm, and present the analysis of its convergence. Extensive experiments on eight synthetic and real-world data-sets are conducted, and the results of UFSRL compared with six corresponding feature selection algorithms. The experimental results show that UFSRL can effectively identify the feature subset with discriminative while reconstructing the data sparsely, and it is superior to some unsupervised feature selection algorithms in clustering performance. More... »

PAGES

757-770

References to SciGraph publications

  • 2012-03. Noise reduction in microarray gene expression data based on spectral analysis in INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
  • 2011. Nonparametric Statistical Inference in INTERNATIONAL ENCYCLOPEDIA OF STATISTICAL SCIENCE
  • 1999-10. Learning the parts of objects by non-negative matrix factorization in NATURE
  • 2013. L 1 Graph Based on Sparse Coding for Feature Selection in ADVANCES IN NEURAL NETWORKS – ISNN 2013
  • 2014-04. Sparse group LASSO based uncertain feature selection in INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
  • 2002-01. Gene Selection for Cancer Classification using Support Vector Machines in MACHINE LEARNING
  • 2014-06. A unified algorithm for mixed l2,p-minimizations and its application in feature selection in COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
  • 2012-12. Null space based feature selection method for gene expression data in INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
  • 2010-12. An efficient gene selection technique for cancer recognition based on neighborhood mutual information in INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
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    http://scigraph.springernature.com/pub.10.1007/s13042-017-0760-y

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