Szabo, Z; (2010) Auto-Regressive Independent Process Analysis without Combinatorial Efforts. Pattern Analysis and Applications , 13 1 - 13. 10.1007/s10044-009-0174-x.

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Abstract

We treat the problem of searching for hidden multi-dimensional independent auto-regressive processes (Auto-Regressive Independent Process Analysis, AR-IPA). Independent Subspace Analysis (ISA) can be used to solve the AR-IPA task. The so-called separation theorem simplifies the ISA task considerably: the theorem enables one to reduce the task to 1-dimensional Blind Source Separation (BSS) task followed by the grouping of the coordinates. However, the grouping of the coordinates still involves 2 types of combinatorial problems: (i) the number of the independent subspaces and their dimensions, and then (ii) the permutation of the estimated coordinates are to be determined. Here, we generalize the separation theorem. We also show a non-combinatorial procedure, which under certain conditions can treat these 2 combinatorial problems. Numerical simulations have been conducted. We investigate problems that fulfill sufficient conditions of the theory and also others that do not. The success of the numerical simulations indicates that further generalizations of the separation theorem may be feasible.

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