Digital processing of random signals: theory and methods
Digital processing of random signals: theory and methods
Jacobi Angles for Simultaneous Diagonalization
SIAM Journal on Matrix Analysis and Applications
Joint Approximate Diagonalization of Positive Definite Hermitian Matrices
SIAM Journal on Matrix Analysis and Applications
The Journal of Machine Learning Research
Joint diagonalization via subspace fitting techniques
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
Sensitivity Analysis for the Problem of Matrix Joint Diagonalization
SIAM Journal on Matrix Analysis and Applications
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
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Nonorthogonal Joint Diagonalization Free of Degenerate Solution
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Blind separation of mixture of independent sources through aquasi-maximum likelihood approach
IEEE Transactions on Signal Processing
Quadratic optimization for simultaneous matrix diagonalization
IEEE Transactions on Signal Processing
IEEE Transactions on Neural Networks
Blind separation of piecewise stationary non-Gaussian sources
Signal Processing
Multidimensional Systems and Signal Processing
QML-based joint diagonalization of positive-definite hermitian matrices
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Adaptive time-domain blind separation of speech signals
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Journal of Cognitive Neuroscience
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
On computation of approximate joint block-diagonalization using ordinary AJD
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Semi-blind source separation based on ICA and overlapped speech detection
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
| Hi-index | 35.69 |
We propose a new low-complexity approximate joint diagonalization (AJD) algorithm, which incorporates nontrivial block-diagonal weight matrices into a weighted least-squares (WLS) AJD criterion. Often in blind source separation (BSS), when the sources are nearly separated, the optimal weight matrix for WLS-based AJD takes a (nearly) block-diagonal form. Based on this observation, we show how the new algorithm can be utilized in an iteratively reweighted separation scheme, thereby giving rise to fast implementation of asymptotically optimal BSS algorithms in various scenarios. In particular, we consider three specific (yet common) scenarios, involving stationary or block-stationary Gaussian sources, for which the optimal weight matrices can be readily estimated from the sample covariance matrices (which are also the target-matrices for the AJD). Comparative simulation results demonstrate the advantages in both speed and accuracy, as well as compliance with the theoretically predicted asymptotic optimality of the resulting BSS algorithms based on the weighted AJD, both on large scale problems with matrices of the size 100 × 100.