Abstract
A new unsupervised algorithm is proposed that performs competitive principal component analysis (PCA) of a time series. A set of expert PCA networks compete, through the mixture of experts (MOE) formalism, on the basis of their ability to reconstruct the original signal. The resulting network finds an optimal projection of the input onto a reduced dimensional space as a function of the input and, hence, of time. As a byproduct, the time series is both segmented and identified according to stationary regions. Examples showing the performance of the algorithm are included.
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