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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 Joint Approximation Diagonalization of Eigen-matrices ： ウィキペディア英語版 Joint Approximation Diagonalization of Eigen-matrices Joint Approximation Diagonalization of Eigen-matrices (JADE) is an algorithm for independent component analysis that separates observed mixed signals into latent source signals by exploiting fourth order moments. The fourth order moments are a measure of non-Gaussianity, which is used as a proxy for defining independence between the source signals. The motivation for this measure is that Gaussian distributions possess zero excess kurtosis, and with non-Gaussianity being a canonical assumption of ICA, JADE seeks an orthogonal rotation of the observed mixed vectors to estimate source vectors which possess high values of excess kurtosis. == Algorithm == Let $\backslash mathbf\; =\; (x\_)\; \backslash in\; \backslash mathbb^$ denote an observed data matrix whose $n$ columns correspond to observations of $m$-variate mixed vectors. It is assumed that $\backslash mathbf$ is ''prewhitenend'', that is, its rows have a sample mean equaling zero and a sample covariance is the $m\; \backslash times\; m$ dimensional identity matrix, that is, $\backslash frac\backslash sum\_^n\; x\_\; =\; 0\; \backslash quad\; \backslash text\; \backslash quad\; \backslash frac\backslash mathbf^\; =\; \backslash mathbf\_m$. Applying JADE to $\backslash mathbf$ entails # computing ''fourth-order cumulants'' of $\backslash mathbf$ and then # optimizing a ''contrast function'' to obtain a $m\; \backslash times\; m$ rotation matrix $O$ to estimate the source components given by the rows of the $m\; \backslash times\; n$ dimensional matrix $\backslash mathbf\; :=\; \backslash mathbf^\; \backslash mathbf$. 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「Joint Approximation Diagonalization of Eigen-matrices」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.