Presentation 2004/6/11
Study of Feature Selection Method by using Structural Similarity
Yujiro ONO, Shinya ISHIKAWA, Atsushi NAGOYA, Manabu ICHINO,
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Abstract(in English) We investigate a feature selection method to detect meaningful structures embedded in multidimensional data. Data analysis reveals two kinds of meaningful structures : one in which individuals are consistent with functional relationships, the other in which individuals make clusters. Our method can select useful features for detecting both structures. In this paper, we define the ratio of Including Samples (IS) on Cartesian System Model (CSM), a mathematical model that can manipulate symbolic data. Additionally, we define Structural Similarity (SS) by using IS. SS can evaluate the similarity between feature sets. By using SS, we propose a feature selection method and show the capabilities by applying it to two sets of artificial and one set of real data.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Feature Selection / Structural Similarity / Cartesian System Model / Clustering
Paper # PRMU2004-34
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Committee PRMU
Conference Date 2004/6/11(1days)
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Paper Information
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) Study of Feature Selection Method by using Structural Similarity
Sub Title (in English)
Keyword(1) Feature Selection
Keyword(2) Structural Similarity
Keyword(3) Cartesian System Model
Keyword(4) Clustering
1st Author's Name Yujiro ONO
1st Author's Affiliation Department of Social Sciences and Information Science, Jumonji University()
2nd Author's Name Shinya ISHIKAWA
2nd Author's Affiliation School of Science and Engineering, Tokyo Denki University
3rd Author's Name Atsushi NAGOYA
3rd Author's Affiliation School of Science and Engineering, Tokyo Denki University
4th Author's Name Manabu ICHINO
4th Author's Affiliation School of Science and Engineering, Tokyo Denki University
Date 2004/6/11
Paper # PRMU2004-34
Volume (vol) vol.104
Number (no) 125
Page pp.pp.-
#Pages 6
Date of Issue