Abstract
In this paper a generalization of Post Nonlinear Independent Component Analysis (PNL-ICA) to Post Nonlinear Independent Subspace Analysis (PNL-ISA) is presented. In this framework sources to be identified can be multidimensional as well. For this generalization we prove a separability theorem: the ambiguities of this problem are essentially the same as for the linear Independent Subspace Analysis (ISA). By applying this result we derive an algorithm using the mirror structure of the mixing system. Numerical simulations are presented to illustrate the efficiency of the algorithm. © Springer-Verlag Berlin Heidelberg 2007.
Cite
CITATION STYLE
Szabó, Z., Póczos, B., Szirtes, G., & Lorincz, A. (2007). Post nonlinear independent subspace analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4668 LNCS, pp. 677–686). Springer Verlag. https://doi.org/10.1007/978-3-540-74690-4_69
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
