基底の前進選択による関数近似用スパースサポートベクトルマシン

カテゴリ: 部門大会

論文No: GS6-4

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): Sparse Support Vector Regressor Based on Forward Selection

著者名: 村岡 重則(神戸大学),阿部 重夫(神戸大学)

著者名(英語): Shigenori Muraoka(Kobe University),Shigeo Abe(Kobe University)

キーワード: 関数近似用サポートベクトルマシン|スパース解|標本特徴空間標本特徴空間|Support Vector Regressors|sparse solution|empirical feature space

要約(日本語): Support Vector Regressors (SVRs) usually give sparse solutions but as a regression problem becomes more difficult the number of support vectors increases and thus sparsity is lost. To solve this problem, in this paper we propose sparse support vector regressors (S-SVRs) trained in the reduced empirical feature space. First by forward selection we select the training data samples, which minimize the regression error estimated by kernel least squares. Then in the reduced empirical feature space spanned by the selected, mapped training data, we train the SVR in the dual form. Since the mapped support vectors obtained by training the S-SVR are expressed by the linear combination of the selected, mapped training data, the support vectors, in the sense that form a solution, are selected training data. By computer simulation, we compare performance of the proposed method with that of the regular SVR and that of the sparse SVR based on Cholesky factorization.

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