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COMPLIFE
2006
Springer

Set-Oriented Dimension Reduction: Localizing Principal Component Analysis Via Hidden Markov Models

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Set-Oriented Dimension Reduction: Localizing Principal Component Analysis Via Hidden Markov Models
We present a method for simultaneous dimension reduction and metastability analysis of high dimensional time series. The approach is based on the combination of hidden Markov models (HMMs) and principal component analysis. We derive optimal estimators for the loglikelihood functional and employ the Expectation Maximization algorithm for its numerical optimization. We demonstrate the performance of the method on a generic 102-dimensional example, apply the new HMM-PCA algorithm to a molecular dynamics simulation of 12
Illia Horenko, Johannes Schmidt-Ehrenberg, Christo
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2006
Where COMPLIFE
Authors Illia Horenko, Johannes Schmidt-Ehrenberg, Christof Schütte
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