Blind identification of MIMO-FIR systems: a generalized linear prediction approach
Signal Processing - Special issue on blind source separation and multichannel deconvolution
Estimation of parameters and eigenmodes of multivariate autoregressive models
ACM Transactions on Mathematical Software (TOMS)
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Undercomplete Blind Subspace Deconvolution
The Journal of Machine Learning Research
Cross-Entropy optimization for independent process analysis
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Multivariate MIMO FIR inverses
IEEE Transactions on Image Processing
Complete Blind Subspace Deconvolution
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Separation theorem for independent subspace analysis and its consequences
Pattern Recognition
Contrast functions for independent subspace analysis
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
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We present a novel solution technique for the blind subspace deconvolution (BSSD) problem, where temporal convolution of multidimensional hidden independent components is observed and the task is to uncover the hidden components using the observation only. We carry out this task for the undercomplete case (uBSSD): we reduce the original uBSSD task via linear prediction to independent subspace analysis (ISA), which we can solve. As it has been shown recently, applying temporal concatenation can also reduce uBSSD to ISA, but the associated ISA problem can easily become `high dimensional' [1]. The new reduction method circumvents this dimensionality problem. We perform detailed studies on the efficiency of the proposed technique by means of numerical simulations. We have found several advantages: our method can achieve high quality estimations for smaller number of samples and it can cope with deeper temporal convolutions.