A Nonlinear Principal Component Analysis of Image Data

Ryo SAEGUSA  Hitoshi SAKANO  Shuji HASHIMOTO  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E88-D   No.10   pp.2242-2248
Publication Date: 2005/10/01
Online ISSN: 
DOI: 10.1093/ietisy/e88-d.10.2242
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Image Recognition and Understanding)
Category: 
Keyword: 
nonlinear PCA,  neural network,  dimensionality reduction,  image,  

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Summary: 
Principal Component Analysis (PCA) has been applied in various areas such as pattern recognition and data compression. In some cases, however, PCA does not extract the characteristics of the data-distribution efficiently. In order to overcome this problem, we have proposed a novel method of Nonlinear PCA which preserves the order of the principal components. In this paper, we reduce the dimensionality of image data using the proposed method, and examine its effectiveness in the compression and recognition of images.