Structured Graph Optimization for Unsupervised Feature Selection

Feiping Nie, Wei Zhu, Xuelong Li

Research output: Contribution to journalArticlepeer-review

90 Scopus citations

Abstract

Unsupervised feature selection has attracted more and more attention due to the rapid growth of the large amount of unlabelled and high-dimensional data. The performance of traditional spectral-based unsupervised methods always depends on the quality of constructed similarity matrix. However, real world data always contain a large number of noise samples and features that make the similarity matrix created by original data cannot be fully relied. We propose an unsupervised feature selection method which conducts feature selection and local structure learning simultaneously. Moreover, we add an important constraint on the similarity matrix to allow it to capture more accurate information of the data structure. To perform feature selection, orthogonal constraint and $\ell _{2,p}$ℓ2,p-norm are adopted on the projection matrix. An efficient and simple algorithm is derived to tackle the problem. We conduct comprehensive experiments on various benchmark data sets, including handwritten digit, face image, and biomedical data, to validate the effectiveness of the proposed approach.

Original languageEnglish
Article number8815854
Pages (from-to)1210-1222
Number of pages13
JournalIEEE Transactions on Knowledge and Data Engineering
Volume33
Issue number3
DOIs
StatePublished - 1 Mar 2021

Keywords

  • Structured optimal graph
  • embedded method
  • manifold learning
  • unsupervised feature selection

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