部分空間法に基づく最小自乗サポートベクトルマシン

カテゴリ: 部門大会

論文No: GS6-6

グループ名: 【C】平成21年電気学会電子・情報・システム部門大会講演論文集

発行日: 2009/09/03

タイトル(英語): Subspace Based Least Squares Support Vector Machines

著者名: 北村 拓也(神戸大学),阿部 重夫(神戸大学),福井 和広(筑波大学)

著者名(英語): Takuya Kitamura(Kobe University),Shigeo Abe(Kobe University),Kazuhiro Fukui(University of Tsukuba)

キーワード: 部分空間法|主成分分析|最小自乗サポートベクトルマシン最小自乗サポートベクトルマシン|subspace methods|principal component analysis|least squares support vector machines

要約(日本語): In this paper, we discuss subspace based least squares support vector machines (SSLS-SVMs), in which an input vector is classified into the class with the maximum similarity. Namely, we define the similarity measure for each class by the weighted sum of vectors called dictionaries and optimize the weights so that the margin between classes is optimized. Because the similarity measure is defined for each class, the similarity measure associated with a data sample needs to be the largest among all the similarity measures. Introducing slack variables we define these constraints by equality constraints. Then the proposed SSLS-SVMs is similar to LS-SVMs by all-at-once formulation. Because all-at-once formulation is inefficient, we also propose SSLS-SVMs by one-against-all formulation. We demonstrate the effectiveness of the proposed methods with the conventional method for two-class problems.

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