L1 graph based on sparse coding for feature selection

Jin Xu, Guang Yang, Hong Man, Haibo He

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

29 Scopus citations

Abstract

In machine learning and pattern recognition, feature selection has been a very active topic in the literature. Unsupervised feature selection is challenging due to the lack of label which would supply the categorical information. How to define an appropriate metric is the key for feature selection. In this paper, we propose a "filter" method for unsupervised feature selection, which is based on the geometry properties of ℓ1 graph. ℓ1 graph is constructed through sparse coding. The graph establishes the relations of feature subspaces and the quality of features is evaluated by features' local preserving ability. We compare our method with classic unsupervised feature selection methods (Laplacian score and Pearson correlation) and supervised method (Fisher score) on benchmark data sets. The classification results based on support vector machine, k-nearest neighbors and multi-layer feed-forward networks demonstrate the efficiency and effectiveness of our method.

Original languageEnglish
Title of host publicationAdvances in Neural Networks, ISNN 2013 - 10th International Symposium on Neural Networks, Proceedings
Pages594-601
Number of pages8
EditionPART 1
DOIs
StatePublished - 2013
Event10th International Symposium on Neural Networks, ISNN 2013 - Dalian, China
Duration: 4 Jul 20136 Jul 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7951 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Symposium on Neural Networks, ISNN 2013
Country/TerritoryChina
CityDalian
Period4/07/136/07/13

Keywords

  • Feature selection
  • Neural network
  • Sparse coding
  • Unsupervised learning

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