A Nonlinear Principal Component Analysis of Image Data(<Special Section>Image Recognition and Understanding)
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概要
- 論文の詳細を見る
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.
- 社団法人電子情報通信学会の論文
- 2005-10-01
著者
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Hashimoto Shuji
Waseda Univ. Tokyo Jpn
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Saegusa Ryo
Department Of Applied Physics School Of Science And Engineering Waseda University
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Sakano Hitoshi
Ntt Data Corporation
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Hashimoto Shuji
Department Of Applied Physics School Of Science And Engineering Waseda University
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Hashimoto Shuji
Department Of Applied Biological Chemistry Faculty Of Agriculture Niigata University
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