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Distribution preserving learning for unsupervised feature selection
Ting Xie1,4; Pengfei Ren2; Taiping Zhang2; Yuan Yan Tang3
2018-05-10
Source PublicationNeurocomputing
ISSN0925-2312
Volume289Pages:231-240
Abstract

Selection of most relevant features from high-dimensional data is difficult especially in unsupervised learning scenario, this is because there is an absence of class labels that would guide the search for relevant features. In this work, we propose a distribution preserving feature selection (DPFS) method for unsupervised feature selection. Specifically, we select those features such that the distribution of the data can be preserved. Theoretical analysis show that our proposed DPFS method share some excellent properties of kernel method. Moreover, traditional "wrapper" and "filter" feature selection methods often involve an exhaustive search optimization, feature selection problem is treated as variable of optimization problem in our proposed method, the optimization is tractable. Extensive experimental results over various real-life data sets have demonstrated the effectiveness of the proposed algorithm. (C) 2018 Elsevier B.V. All rights reserved.

KeywordFeature Selection Density Preserving Kernel Density Estimation Dimensionality Reduction Data Mining
DOIhttps://doi.org/10.1016/j.neucom.2018.02.032
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000428123200019
PublisherELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
The Source to ArticleWOS
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorTaiping Zhang
Affiliation1.College of Mathematics and Statistics, Chongqing University, 400030 Chongqing, China
2.College of Computer Science, Chongqing University, 400030 Chongqing, China
3.Faculty of Science and Technology, University of Macau, Macau, China
4.College of Science, Chongqing University of Technology, 400054 Chongqing, China
Recommended Citation
GB/T 7714
Ting Xie,Pengfei Ren,Taiping Zhang,et al. Distribution preserving learning for unsupervised feature selection[J]. Neurocomputing,2018,289:231-240.
APA Ting Xie,Pengfei Ren,Taiping Zhang,&Yuan Yan Tang.(2018).Distribution preserving learning for unsupervised feature selection.Neurocomputing,289,231-240.
MLA Ting Xie,et al."Distribution preserving learning for unsupervised feature selection".Neurocomputing 289(2018):231-240.
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