Unsupervised feature selection using weighted principal components

Seoung Bum Kim, Panaya Rattakorn

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)


Feature selection has received considerable attention in various areas as a way to select informative features and to simplify the statistical model through dimensional reduction. One of the most widely used methods for dimensional reduction includes principal component analysis (PCA). Despite its popularity, PCA suffers from a lack of interpretability of the original feature because the reduced dimensions are linear combinations of a large number of original features. Traditionally, two or three dimensional loading plots provide information to identify important original features in the first few principal component dimensions. However, the interpretation of what constitutes a loading plot is frequently subjective, particularly when large numbers of features are involved. In this study, we propose an unsupervised feature selection method that combines weighted principal components (PCs) with a thresholding algorithm. The weighted PC is obtained by the weighted sum of the first k PCs of interest. Each of the k loading values in the weighted PC reflects the contribution of each individual feature. We also propose a thresholding algorithm that identifies the significant features. Our experimental results with both the simulated and real datasets demonstrated the effectiveness of the proposed unsupervised feature selection method.

Original languageEnglish
Pages (from-to)5704-5710
Number of pages7
JournalExpert Systems With Applications
Issue number5
Publication statusPublished - 2011 May

Bibliographical note

Funding Information:
This work was support in part by Grant No. 2010003811 from the National Research Foundation of Korea.

Copyright 2011 Elsevier B.V., All rights reserved.


  • Data mining
  • Feature selection
  • Principal component analysis
  • Unsupervised learning

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence


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