Skip Navigation

IEICE Transactions on Information and Systems 2005 E88-D(10):2242-2248; doi:10.1093/ietisy/e88-d.10.2242
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Request Permissions
Google Scholar
Right arrow Articles by SAEGUSA, R.
Right arrow Articles by HASHIMOTO, S.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Copyright © 2005 The Institute of Electronics, Information and Communication Engineers

Special Section on Image Recognition and Understanding -- Papers

A Nonlinear Principal Component Analysis of Image Data

Ryo SAEGUSA1, Hitoshi SAKANO2 and Shuji HASHIMOTO1

1 The authors are with the Department of Applied Physics, School of Science and Engineering, Waseda University, Tokyo, 169–8555 Japan. E-mail: ryos{at}ieee.org, E-mail: shuji{at}shalab.phys.waseda.ac.jp, 2 The author is with NTT Data Corporation, Tokyo, 104–0033 Japan. E-mail: sakanoh{at}nttdata.co.jp

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.

Key Words: nonlinear PCA, neural network, dimensionality reduction, image

Manuscript received September 27, 2004. Manuscript revised February 7, 2005.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.