Adaptive Weighted Sparse Principal Component Analysis for Robust Unsupervised Feature Selection

Shuangyan Yi, Zhenyu He, Xiao Yuan Jing*, Yi Li, Yiu Ming Cheung, Feiping Nie

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

32 Citations (Scopus)


Current unsupervised feature selection methods cannot well select the effective features from the corrupted data. To this end, we propose a robust unsupervised feature selection method under the robust principal component analysis (PCA) reconstruction criterion, which is named the adaptive weighted sparse PCA (AW-SPCA). In the proposed method, both the regularization term and the reconstruction error term are constrained by the $\ell _{2,1}$ -norm: the $\ell _{2,1}$ -norm regularization term plays a role in the feature selection, while the $\ell _{2,1}$ -norm reconstruction error term plays a role in the robust reconstruction. The proposed method is in a convex formulation, and the selected features by it can be used for robust reconstruction and clustering. Experimental results demonstrate that the proposed method can obtain better reconstruction and clustering performance, especially for the corrupted data.

Original languageEnglish
Article number8818654
Pages (from-to)2153-2163
Number of pages11
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number6
Publication statusPublished - Jun 2020

Scopus Subject Areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

User-Defined Keywords

  • clustering
  • feature selection
  • l2,1-norm
  • reconstruction


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