A method for speeding up feature extraction based on KPCA

Yong Xu, Dapeng Zhang, Fengxi Song, Jing Yu Yang, Zhong Jing, Miao Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

123 Citations (Scopus)

Abstract

Kernel principal component analysis (KPCA) extracts features of samples with an efficiency in inverse proportion to the size of the training sample set. In this paper, we develop a novel method to improve KPCA-based feature extraction. The developed method is the first one that is methodologically consistent with KPCA. Experiments on several benchmark datasets illustrate that the feature extraction process derived from the novel method is much more efficient than that associated with KPCA. Moreover, the classification accuracy generated from the developed method is similar to that of KPCA.
Original languageEnglish
Pages (from-to)1056-1061
Number of pages6
JournalNeurocomputing
Volume70
Issue number4-6
DOIs
Publication statusPublished - 1 Jan 2007

Keywords

  • Feature extraction
  • Improved KPCA (IKPCA)
  • Kernel PCA (KPCA)
  • Principal component analysis (PCA)

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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