Characteristic-function-based independent component analysis
Signal Processing - Special section: Security of data hiding technologies
Blind source separation via generalized eigenvalue decomposition
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Blind source separation via generalized eigenvalue decomposition
The Journal of Machine Learning Research
On the use of sparse signal decomposition in the analysis of multi-channel surface electromyograms
Signal Processing - Sparse approximations in signal and image processing
Blind identification of under-determined mixtures based on the characteristic function
Signal Processing - Signal processing in UWB communications
Tensor-based techniques for the blind separation of DS-CDMA signals
Signal Processing
Distributed Jacobi joint diagonalization on clusters of personal computers
International Journal of Parallel Programming
Joint low-rank approximation for extracting non-Gaussian subspaces
Signal Processing
Noise cancellation with static mixtures of a nonstationary signal and stationary noise
EURASIP Journal on Applied Signal Processing
Blind separation of nonstationary sources based on spatial time-frequency distributions
EURASIP Journal on Applied Signal Processing
Finite sample effects of the fast ICA algorithm
Neurocomputing
Astrophysical image separation by blind time--frequency source separation methods
Digital Signal Processing
Soft Dimension Reduction for ICA by Joint Diagonalization on the Stiefel Manifold
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Fast approximate joint diagonalization incorporating weight matrices
IEEE Transactions on Signal Processing
Nonorthogonal joint diagonalization by combining givens and hyperbolic rotations
IEEE Transactions on Signal Processing
A simple overcomplete ICA algorithm by non-orthogonal pair optimizations
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Nonorthogonal approximate joint diagonalization with well-conditioned diagonalizers
IEEE Transactions on Neural Networks
Blind underdetermined mixture identification by joint canonical decomposition of HO cumulants
IEEE Transactions on Signal Processing
On blind separability based on the temporal predictability method
Neural Computation
Multidimensional Systems and Signal Processing
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Non unitary joint block diagonalization of complex matrices using a gradient approach
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Joint diagonalization of kernels for information fusion
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
PARAFAC2 receivers for orthogonal space-time block codes
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Learning by natural gradient on noncompact matrix-type pseudo-Riemannian manifolds
IEEE Transactions on Neural Networks
Batch and adaptive PARAFAC-based blind separation of convolutive speech mixtures
IEEE Transactions on Audio, Speech, and Language Processing
QML-based joint diagonalization of positive-definite hermitian matrices
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Joint eigenvalue decomposition using polar matrix factorization
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Joint SVD and its application to factorization method
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Functional MRI analysis by a novel spatiotemporal ICA algorithm
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Simple LU and QR based non-orthogonal matrix joint diagonalization
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Second-Order blind identification of underdetermined mixtures
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Separation of periodically time-varying mixtures using second-order statistics
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Complex non-orthogonal joint diagonalization based on LU and LQ decompositions
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
An algebraic method for approximate rank one factorization of rank deficient matrices
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
A combination of parallel factor and independent component analysis
Signal Processing
Novel Blind Carrier Frequency Offset Estimation Algorithm for OFDM System Via Generalized Precoding
Wireless Personal Communications: An International Journal
Coupled Matrix Factorization with Sparse Factors to Identify Potential Biomarkers in Metabolomics
International Journal of Knowledge Discovery in Bioinformatics
Coupled Matrix Factorization with Sparse Factors to Identify Potential Biomarkers in Metabolomics
International Journal of Knowledge Discovery in Bioinformatics
A parallel dual matrix method for blind signal separation
Neural Computation
Hi-index | 35.70 |
Approximate joint diagonalization of a set of matrices is an essential tool in many blind source separation (BSS) algorithms. A common measure of the attained diagonalization of the set is the weighted least-squares (WLS) criterion. However, most well-known algorithms are restricted to finding an orthogonal diagonalizing matrix, relying on a whitening phase for the nonorthogonal factor. Often, such an approach implies unbalanced weighting, which can result in degraded performance. We propose an iterative alternating-directions algorithm for minimizing the WLS criterion with respect to a general (not necessarily orthogonal) diagonalizing matrix. Under some mild assumptions, we prove weak convergence in the sense that the norm of parameters update is guaranteed to fall below any arbitrarily small threshold within a finite number of iterations. We distinguish between Hermitian and symmetrical problems. Using BSS simulations results, we demonstrate the improvement in estimating the mixing matrix, resulting from the relaxation of the orthogonality restriction