Presentation 2004/3/11
Multi-class Pattern Recognition based on Compression of High Dimensional Features
Yusuke FUJIKAWA, Ken'ichi MOROOKA, Hiroshi NAGAHASHI,
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Abstract(in English) This paper proposes a framework for multi-class pattern recognition based on compression of high dimensional features. In pattern recognition, since it is generally difficult to specify features that are suitable for classification of each class in advance, it is important to take the features into consideration broadly from various viewpoints in feature extraction from input pattern. However, if high dimensional features acquired as a result are directly used for classification, various problems such as the explosion of amount of calculation will be caused. Then, high dimensional features in which many features considered to be useful in classification were included are effectively compressed by a neural network to low dimensional features. This realizes highly precise and high-speed recognition.
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Keyword(in English) multi-class pattern / high dimensional features / kernel method / dimensional compression
Paper # PRMU2003-272
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Committee PRMU
Conference Date 2004/3/11(1days)
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Registration To Pattern Recognition and Media Understanding (PRMU)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Multi-class Pattern Recognition based on Compression of High Dimensional Features
Sub Title (in English)
Keyword(1) multi-class pattern
Keyword(2) high dimensional features
Keyword(3) kernel method
Keyword(4) dimensional compression
1st Author's Name Yusuke FUJIKAWA
1st Author's Affiliation Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology()
2nd Author's Name Ken'ichi MOROOKA
2nd Author's Affiliation Imaging Science and Engineering Laboratory, Tokyo Institute of Technology
3rd Author's Name Hiroshi NAGAHASHI
3rd Author's Affiliation Imaging Science and Engineering Laboratory, Tokyo Institute of Technology
Date 2004/3/11
Paper # PRMU2003-272
Volume (vol) vol.103
Number (no) 737
Page pp.pp.-
#Pages 6
Date of Issue