Elements of information theory
Elements of information theory
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
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Support vector machines, reproducing kernel Hilbert spaces, and randomized GACV
Advances in kernel methods
The Geometry of Algorithms with Orthogonality Constraints
SIAM Journal on Matrix Analysis and Applications
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Nonlinear eigenvalue problems
Kernel independent component analysis
The Journal of Machine Learning Research
Beyond independent components: trees and clusters
The Journal of Machine Learning Research
Independent subspace analysis using geodesic spanning trees
ICML '05 Proceedings of the 22nd international conference on Machine learning
Source Recovery for Body Sensor Network
BSN '06 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
Kernel Methods for Measuring Independence
The Journal of Machine Learning Research
Statistical Consistency of Kernel Canonical Correlation Analysis
The Journal of Machine Learning Research
Blind image deconvolution via dispersion minimization
Digital Signal Processing
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
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
ICA and ISA using Schweizer-Wolff measure of dependence
Proceedings of the 25th international conference on Machine learning
Complete Blind Subspace Deconvolution
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Autoregressive model of the hippocampal representation of events
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Controlled complete ARMA independent process analysis
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Separation theorem for independent subspace analysis and its consequences
Pattern Recognition
Distributed high dimensional information theoretical image registration via random projections
Digital Signal Processing
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We introduce the blind subspace deconvolution (BSSD) problem, which is the extension of both the blind source deconvolution (BSD) and the independent subspace analysis (ISA) tasks. We examine the case of the undercomplete BSSD (uBSSD). Applying temporal concatenation we reduce this problem to ISA. The associated `high dimensional' ISA problem can be handled by a recent technique called joint f-decorrelation (JFD). Similar decorrelation methods have been used previously for kernel independent component analysis (kernel-ICA). More precisely, the kernel canonical correlation (KCCA) technique is a member of this family, and, as is shown in this paper, the kernel generalized variance (KGV) method can also be seen as a decorrelation method in the feature space. These kernel based algorithms will be adapted to the ISA task. In the numerical examples, we (i) examine how efficiently the emerging higher dimensional ISA tasks can be tackled, and (ii) explore the working and advantages of the derived kernel-ISA methods.