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The goal of this paper is to extend independent subspace analysis (ISA) tothe case of (i) nonparametric, not strictly stationary source dynamics and (ii)unknown source component dimensions. We make use of functional autoregressive(fAR) processes to model the temporal evolution of the hidden sources. Anextension of the ISA separation principle--which states that the ISA problemcan be solved by traditional independent component analysis (ICA) andclustering of the ICA elements--is derived for the solution of the defined fARindependent process analysis task (fAR-IPA): applying fAR identification wereduce the problem to ISA. A local averaging approach, the Nadaraya-Watsonkernel regression technique is adapted to obtain strongly consistent fARestimation. We extend the Amari-index to different dimensional components andillustrate the efficiency of the fAR-IPA approach by numerical examples.

Towards Nonstationary, Nonparametric Independent Process Analysis with Unknown Source Component Dimensions