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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 Stationary subspace analysis ： ウィキペディア英語版 Stationary subspace analysis Stationary Subspace Analysis (SSA)〔 von Bünau P, Meinecke F C, Király F J, Müller K-R (2009). (Finding Stationary Subspaces in Multivariate Time Series ) ''Phys. Rev. Letter'' 103, 214101. 〕 is a blind source separation algorithm which factorizes a multivariate time series into stationary and non-stationary components. == Introduction == In many settings, the measured time series contains contributions from various underlying sources that cannot be measured directly. For instance, in EEG analysis, the electrodes on the scalp record the activity of a large number of sources located inside the brain.〔Niedermeyer E, da Silva F L. Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincott Williams & Wilkins, 2004. ISBN 0-7817-5126-8〕 These sources can be stationary or non-stationary, but they are not discernible in the electrode signals, which are a mixture of these sources. SSA allows the separation of the stationary from the non-stationary sources in an observed time series. According to the SSA model,〔 the observed multivariate time series $x(t)$ is assumed to be generated as a linear superposition of stationary sources $s^\backslash mathfrak(t)$ and non-stationary sources $s^\backslash mathfrak(t)$, :$$ x(t) = A s(t) = \begin A^\mathfrak & A^\mathfrak \end \begin s^\mathfrak(t) \\ s^\mathfrak(t) \\ \end, where $A$ is an unknown but time-constant mixing matrix; $A^\backslash mathfrak$ and $A^\backslash mathfrak$ are the basis of the stationary and non-stationary subspace respectively. Given samples from the time series $x(t)$, the aim of Stationary Subspace Analysis is to estimate the inverse mixing matrix $A^$ separating the stationary from non-stationary sources in the mixture $x(t)$. 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「Stationary subspace 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.