Dimension Reduction by Local Principal Component Analysis

Nandakishore Kambhatla, Todd K. Leen

Research output: Contribution to journalArticlepeer-review

546 Scopus citations


Reducing or eliminating statistical redundancy between the components of high-dimensional vector data enables a lower-dimensional representation without significant loss of information. Recognizing the limitations of principal component analysis (PCA), researchers in the statistics and neural network communities have developed nonlinear extensions of PCA. This article develops a local linear approach to dimension reduction that provides accurate representations and is fast to compute. We exercise the algorithms on speech and image data, and compare performance with PCA and with neural network implementations of nonlinear PCA. We find that both nonlinear techniques can provide more accurate representations than PCA and show that the local linear techniques outperform neural network implementations.

Original languageEnglish (US)
Pages (from-to)1493-1516
Number of pages24
JournalNeural Computation
Issue number7
StatePublished - Oct 1 1997
Externally publishedYes

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Cognitive Neuroscience


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