Slow feature analysis: unsupervised learning of invariances
Neural Computation
A unifying model for blind separation of independent sources
Signal Processing
Constrained subspace ica based on mutual information optimization directly
Neural Computation
Joint blind source separation by multiset canonical correlation analysis
IEEE Transactions on Signal Processing
Handbook of Blind Source Separation: Independent Component Analysis and Applications
Handbook of Blind Source Separation: Independent Component Analysis and Applications
Nonorthogonal independent vector analysis using multivariate Gaussian model
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Extracting coactivated features from multiple data sets
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
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We consider an extension of ICA and BSS for separating mutually dependent and independent components from two related data sets. We propose a new method which first uses canonical correlation analysis for detecting subspaces of independent and dependent components. Different ICA and BSS methods can after this be used for final separation of these components. Our method has a sound theoretical basis, and it is straightforward to implement and computationally not demanding. Experimental results on synthetic and real-world fMRI data sets demonstrate its good performance.