PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Auto-regressive independent process analysis without combinatorial efforts
Zoltan Szabo, Barnabas Poczos and András Lorincz
Pattern Analysis and Applications Volume 13, Number 1, pp. 1-13, 2010.

There is a more recent version of this eprint available. Click here to view it.


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 one-dimensional blind source separation task followed by the grouping of the coordinates. However, the grouping of the coordinates still involves two types of combinatorial problems: (a) the number of the independent subspaces and their dimensions, and then (b) 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 two 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.

PDF - PASCAL Members only - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Additional Information:
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:8378
Deposited By:Zoltan Szabo
Deposited On:01 December 2011

Available Versions of this Item