オンライン特徴抽出を行う追加型再帰フィッシャー線形判別の改良

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2011/07/01

タイトル(英語): An Improvement of Incremental Recursive Fisher Linear Discriminant for Online Feature Extraction

著者名: 太田　良平（神戸大学大学院工学研究科），小澤　誠一（神戸大学大学院工学研究科）

著者名(英語): Ryohei Ohta (Graduate School of Engineering, Kobe University), Seiichi Ozawa (Graduate School of Engineering, Kobe University)

キーワード: パターン認識，特徴抽出，追加学習，線形判別分析　　Pattern Recognition，Feature Extraction，Incremental Learning，Linear Discriminant Analysis

要約(英語): This paper proposes a new online feature extraction method called Incremental Recursive Fisher Linear Discriminant (IRFLD) whose batch learning algorithm called RFLD has been proposed by Xiang et al. In the conventional Linear Discriminant Analysis (LDA), the number of discriminant vectors is limited to the number of classes minus one due to the rank of the between-class covariance matrix. However, RFLD and the proposed IRFLD can break this limit; that is, an arbitrary number of discriminant vectors can be obtained. In the proposed IRFLD, the Pang et al.'s Incremental Linear Discriminant Analysis (ILDA) is extended such that effective discriminant vectors are recursively searched for the complementary space of a conventional discriminant subspace. In addition, to estimate a suitable number of effective discriminant vectors, the classification accuracy is evaluated with a cross-validation method in an online manner. For this purpose, validation data are obtained by performing the k-means clustering against incoming training data and previous validation data. The performance of IRFLD is evaluated for 16 benchmark data sets. The experimental results show that the final classification accuracies of IRFLD are always better than those of ILDA. We also reveal that this performance improvement is attained by adding discriminant vectors in a complementary LDA subspace.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.131 No.7 （2011） 特集：平成22年電気学会電子・情報・システム部門大会

本誌掲載ページ: 1368-1376 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/131/7/131_7_1368/_article/-char/ja/