Independent subspace analysis using geodesic spanning trees
ICML '05 Proceedings of the 22nd international conference on Machine learning
Edgeworth Approximation of Multivariate Differential Entropy
Neural Computation
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
Independent subspace analysis on innovations
ECML'05 Proceedings of the 16th European conference on Machine Learning
Dual multivariate auto-regressive modeling in state space for temporal signal separation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Joint low-rank approximation for extracting non-Gaussian subspaces
Signal Processing
Undercomplete Blind Subspace Deconvolution
The Journal of Machine Learning Research
Undercomplete Blind Subspace Deconvolution Via Linear Prediction
ECML '07 Proceedings of the 18th European 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
Learning to play using low-complexity rule-based policies: illustrations through Ms. Pac-Man
Journal of Artificial Intelligence Research
Controlled complete ARMA independent process analysis
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Independent process analysis without a priori dimensional information
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Sparse and silent coding in neural circuits
Neurocomputing
Separation theorem for independent subspace analysis and its consequences
Pattern Recognition
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We treat the problem of searching for hidden multi-dimensional independent auto-regressive processes. First, we transform the problem to Independent Subspace Analysis (ISA). Our main contribution concerns ISA. We show that under certain conditions, ISA is equivalent to a combinatorial optimization problem. For the solution of this optimization we apply the cross-entropy method. Numerical simulations indicate that the cross-entropy method can provide considerable improvements over other state-of-the-art methods.