Locally principal component learning for face representation and recognition

Jian Yang, Dapeng Zhang, Jing yu Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)

Abstract

This paper develops a method called locally principal component analysis (LPCA) for data representation. LPCA is a linear and unsupervised subspace-learning technique, which focuses on the data points within local neighborhoods and seeks to discover the local structure of data. This local structure may contain useful information for discrimination. LPCA is tested and evaluated using the AT&T face database. The experimental results show that LPCA is effective for dimension reduction and more powerful than PCA for face recognition.
Original languageEnglish
Pages (from-to)1697-1701
Number of pages5
JournalNeurocomputing
Volume69
Issue number13-15
DOIs
Publication statusPublished - 1 Aug 2006

Keywords

  • Dimensionality reduction
  • Face recognition
  • Feature extraction
  • Locality-based learning
  • Principal component analysis (PCA)

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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