@inproceedings{2043ed3363a145bcb28e80863aa86ae7,
title = "Towards a general independent subspace analysis",
abstract = "The increasingly popular independent component analysis (ICA) may only be applied to data following the generative ICA model in order to guarantee algorithm-independent and theoretically valid results. Subspace ICA models generalize the assumption of component independence to independence between groups of components. They are attractive candidates for dimensionality reduction methods, however are currently limited by the assumption of equal group sizes or less general semi-parametric models. By introducing the concept of irreducible independent subspaces or components, we present a generalization to a parameter-free mixture model. Moreover, we relieve the condition of at-most-one-Gaussian by including previous results on non-Gaussian component analysis. After introducing this general model, we discuss joint block diagonalization with unknown block sizes, on which we base a simple extension of JADE to algorithmically perform the subspace analysis. Simulations confirm the feasibility of the algorithm.",
author = "Theis, {Fabian J.}",
note = "Publisher Copyright: {\textcopyright} NIPS 2006.All rights reserved; 19th International Conference on Neural Information Processing Systems, NIPS 2006 ; Conference date: 04-12-2006 Through 07-12-2006",
year = "2006",
language = "English",
series = "NIPS 2006: Proceedings of the 19th International Conference on Neural Information Processing Systems",
publisher = "MIT Press Journals",
pages = "1361--1368",
editor = "Bernhard Scholkopf and Platt, {John C.} and Thomas Hofmann",
booktitle = "NIPS 2006",
}