Matrix analysis
A neural cocktail-party processor
Biological Cybernetics
SIAM Journal on Scientific and Statistical Computing
Common principal components & related multivariate models
Common principal components & related multivariate models
Numerical methods for simultaneous diagonalization
SIAM Journal on Matrix Analysis and Applications
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A neural net for blind separation of nonstationary signals
Neural Networks
Jacobi Angles for Simultaneous Diagonalization
SIAM Journal on Matrix Analysis and Applications
Independent component analysis for identification of artifacts in magnetoencephalographic recordings
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
High-order contrasts for independent component analysis
Neural Computation
Blind source separation via the second characteristic function
Signal Processing
Joint Approximate Diagonalization of Positive Definite Hermitian Matrices
SIAM Journal on Matrix Analysis and Applications
Kernel-based nonlinear blind source separation
Neural Computation
Nonholonomic Orthogonal Learning Algorithms for Blind Source Separation
Neural Computation
Joint diagonalization via subspace fitting techniques
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Joint angle and delay estimation using shift-invariance techniques
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixtures of nonstationary sources
IEEE Transactions on Signal Processing
A generalization of joint-diagonalization criteria for sourceseparation
IEEE Transactions on Signal Processing
Least Square Joint Diagonalization of Matrices under an Intrinsic Scale Constraint
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
Nonorthogonal approximate joint diagonalization with well-conditioned diagonalizers
IEEE Transactions on Neural Networks
On blind separability based on the temporal predictability method
Neural Computation
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
Physical-layer network coding over wireless fading channel
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
QML-based joint diagonalization of positive-definite hermitian matrices
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Joint SVD and its application to factorization method
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
A new discriminant subspace analysis approach for multi-class problems
Pattern Recognition
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
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
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
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A new efficient algorithm is presented for joint diagonalization of several matrices. The algorithm is based on the Frobenius-norm formulation of the joint diagonalization problem, and addresses diagonalization with a general, non-orthogonal transformation. The iterative scheme of the algorithm is based on a multiplicative update which ensures the invertibility of the diagonalizer. The algorithm's efficiency stems from the special approximation of the cost function resulting in a sparse, block-diagonal Hessian to be used in the computation of the quasi-Newton update step. Extensive numerical simulations illustrate the performance of the algorithm and provide a comparison to other leading diagonalization methods. The results of such comparison demonstrate that the proposed algorithm is a viable alternative to existing state-of-the-art joint diagonalization algorithms. The practical use of our algorithm is shown for blind source separation problems.