Recently, several algorithms have been proposed for independent subspace analysis where hidden variables are i.i.d. processes. We show that these methods can be extended to certain AR, MA, ARMA and ARIMA tasks. Central to our paper is that we introduce a cascade of algorithms, which aims to solve these tasks without previous knowledge about the number and the dimensions of the hidden processes. Our claim is supported by numerical simulations. As an illustrative application where the dimensions of the hidden variables are unknown, we search for subspaces of facial components. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Póczos, B., Szabó, Z., Kiszlinger, M., & Lorincz, A. (2007). Independent process analysis without a priori dimensional information. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4666 LNCS, pp. 252–259). Springer Verlag. https://doi.org/10.1007/978-3-540-74494-8_32
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