Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A fast fixed-point algorithm for independent component analysis
Neural Computation
Blind source separation via the second characteristic function
Signal Processing
Linear geometric ICA: fundamentals and algorithms
Neural Computation
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
A new concept for separability problems in blind source separation
Neural Computation
In Search of Non-Gaussian Components of a High-Dimensional Distribution
The Journal of Machine Learning Research
Depth-first search and linear grajh algorithms
SWAT '71 Proceedings of the 12th Annual Symposium on Switching and Automata Theory (swat 1971)
Independent subspace analysis is unique, given irreducibility
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Subspaces of spatially varying independent components in fMRI
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Statistical analysis of sample-size effects in ICA
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated 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
Riemannian optimization method on the flag manifold for independent subspace analysis
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Average convergence behavior of the FastICA algorithm for blind source separation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Uniqueness of non-gaussian subspace analysis
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Compact CramÉr–Rao Bound Expression for Independent Component Analysis
IEEE Transactions on Signal Processing
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
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Given a random vector X, we address the question of linear separability of X, that is, the task of finding a linear operator W such that we have (S"1,...,S"M)=(WX) with statistically independent random vectors S"i. As this requirement alone is already fulfilled trivially by X being independent of the empty rest, we require that the components be not further decomposable. We show that if X has finite covariance, such a representation is unique up to trivial indeterminacies. We propose an algorithm based on this proof and demonstrate its applicability. Related algorithms, however with fixed dimensionality of the subspaces, have already been successfully employed in biomedical applications, such as separation of fMRI recorded data. Based on the presented uniqueness result, it is now clear that also subspace dimensions can be determined in a unique and therefore meaningful fashion, which shows the advantages of independent subspace analysis in contrast to methods like principal component analysis.