Data dimensionality reduction approach to improve feature selection performance using sparsified SVD

Pengpeng Lin, Jun Zhang, Ran An

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

4 Scopus citations

Abstract

Feature selection is a technique of selecting a subset of relevant features for building robust learning models. In this paper, we developed a data dimensionality reduction approach using sparsified singular value decomposition (SSVD) technique to identify and remove trivial features before applying any advanced feature selection algorithm. First, we investigated how SSVD can be used to identify and remove nonessential features in order to facilitate feature selection performance. Second, we analyzed the application limitations and computing complexity. Next, a set of experiments were conducted and the empirical results show that applying feature selection techniques on the data of which the nonessential features are removed by the data dimensionality reduction approach generally results in better performance with significantly reduced computing time.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Pages1393-1400
Number of pages8
ISBN (Electronic)9781479914845
DOIs
StatePublished - Sep 3 2014
Event2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China
Duration: Jul 6 2014Jul 11 2014

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2014 International Joint Conference on Neural Networks, IJCNN 2014
Country/TerritoryChina
CityBeijing
Period7/6/147/11/14

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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