A nonlinear principal component analysis on image data

Ryo Saegusa; Hitoshi Sakano; Shuji Hashimoto

A nonlinear principal component analysis on image data

Ryo Saegusa^*, Hitoshi Sakano, Shuji Hashimoto

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

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 characteristic 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 principal components. In this paper, we reduce the dimensionality of image data with the proposed method, and examine its effectiveness in compression and recognition of the images.

Original language	English
Title of host publication	Machine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop
Editors	A. Barros, J. Principe, J. Larsen, T. Adali, S. Douglas
Pages	589-598
Number of pages	10
Publication status	Published - 2004
Event	Machine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop - Sao Luis Duration: 2004 Sept 29 → 2004 Oct 1

Other

Other	Machine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop
City	Sao Luis
Period	04/9/29 → 04/10/1

ASJC Scopus subject areas

Engineering(all)

Cite this

A nonlinear principal component analysis on image data. / Saegusa, Ryo; Sakano, Hitoshi; Hashimoto, Shuji.

Machine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop. ed. / A. Barros; J. Principe; J. Larsen; T. Adali; S. Douglas. 2004. p. 589-598.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Saegusa, R, Sakano, H & Hashimoto, S 2004, A nonlinear principal component analysis on image data. in A Barros, J Principe, J Larsen, T Adali & S Douglas (eds), Machine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop. pp. 589-598, Machine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop, Sao Luis, 04/9/29.

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abstract = "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 characteristic 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 principal components. In this paper, we reduce the dimensionality of image data with the proposed method, and examine its effectiveness in compression and recognition of the images.",

author = "Ryo Saegusa and Hitoshi Sakano and Shuji Hashimoto",

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AU - Sakano, Hitoshi

AU - Hashimoto, Shuji

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AB - 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 characteristic 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 principal components. In this paper, we reduce the dimensionality of image data with the proposed method, and examine its effectiveness in compression and recognition of the images.

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M3 - Conference contribution

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BT - Machine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop

A2 - Barros, A.

A2 - Principe, J.

A2 - Larsen, J.

A2 - Adali, T.

A2 - Douglas, S.

T2 - Machine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop

Y2 - 29 September 2004 through 1 October 2004

ER -

A nonlinear principal component analysis on image data

Abstract

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