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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Regularized canonical correlation analysis ： ウィキペディア英語版 Regularized canonical correlation analysis Regularized canonical correlation analysis is a way of using ridge regression to solve the singularity problem in the cross-covariance matrices of canonical correlation analysis. By converting $\backslash operatorname(X,\; X)$ and $\backslash operatorname(Y,\; Y)$ into $\backslash operatorname(X,\; X)\; +\; \backslash lambda\; I\_X$ and $\backslash operatorname(Y,\; Y)\; +\; \backslash lambda\; I\_Y$, it ensures that the above matrices will have reliable inverses. The idea probably dates back to Hrishikesh D. Vinod's publication in 1976 where he called it "Canonical ridge". It has been suggested for use in the analysis of functional neuroimaging data as such data are often singular. It is possible to compute the regularized canonical vectors in the lower-dimensional space.〔 Section 3.18.5〕 == References == * 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Regularized canonical correlation analysis」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.