Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Independent component analysis for identification of artifacts in magnetoencephalographic recordings
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
ACM Transactions on Mathematical Software (TOMS)
Independent subspace analysis using geodesic spanning trees
ICML '05 Proceedings of the 22nd international conference on Machine learning
Independent subspace analysis using k-nearest neighborhood distances
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Dual multivariate auto-regressive modeling in state space for temporal signal separation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Independent process analysis without a priori dimensional information
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Separation theorem for independent subspace analysis and its consequences
Pattern Recognition
Cross-Entropy optimization for independent process analysis
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
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Independent subspace analysis (ISA) that deals with multi-dimensional independent sources, is a generalization of independent component analysis (ICA). However, all known ISA algorithms may become ineffective when the sources possess temporal structure. The innovation process instead of the original mixtures has been proposed to solve ICA problems with temporal dependencies. Here we show that this strategy can be applied to ISA as well. We demonstrate the idea on a mixture of 3D processes and also on a mixture of facial pictures used as two-dimensional deterministic sources. ISA on innovations was able to find the original subspaces, while plain ISA was not.