Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Estimation of parameters and eigenmodes of multivariate autoregressive models
ACM Transactions on Mathematical Software (TOMS)
ACM Transactions on Mathematical Software (TOMS)
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
View-Subspace Analysis of Multi-View Face Patterns
RATFG-RTS '01 Proceedings of the IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems (RATFG-RTS'01)
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
Undercomplete Blind Subspace Deconvolution
The Journal of Machine Learning Research
ICA and ISA using Schweizer-Wolff measure of dependence
Proceedings of the 25th international conference on Machine learning
Blind image deconvolution via dispersion minimization
Digital Signal Processing
A flexible component model for precision ICA
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Blind separation of instantaneous mixtures of dependent sources
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Independent process analysis without a priori dimensional information
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
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
Tree-Dependent components of gene expression data for clustering
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - 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
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
| Hi-index | 0.00 |
In this paper we address the controlled complete AutoRegressive Moving Average Independent Process Analysis (ARMAX-IPA; X-exogenous input or control) problem, which is a generalization of the Blind SubSpace Deconvolution (BSSD) task. Compared to our previous work that dealt with the undercomplete situation, (i) here we extend the theory to complete systems, (ii) allow an autoregressive part to be present, (iii) and include exogenous control. We investigate the case when the observed signal is a linear mixture of independent multidimensional ARMA processes that can be controlled. Our objective is to estimate the ARMA processes, their driving noises as well as the mixing. We aim efficient estimation by choosing suitable control values. For the optimal choice of the control we adapt the D-optimality principle, also known as the 'InfoMax method'. We solve the problem by reducing it to a fully observable D-optimal ARX task and Independent Subspace Analysis (ISA) that we can solve. Numerical examples illustrate the efficiency of the proposed method.