The Journal of Machine Learning Research
Fast approximate joint diagonalization incorporating weight matrices
IEEE Transactions on Signal Processing
Blind separation of mutually correlated sources using precoders
IEEE Transactions on Neural Networks
QML-based joint diagonalization of positive-definite hermitian matrices
IEEE Transactions on Signal Processing
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
Hi-index | 0.01 |
Joint diagonalization problems of Hermitian or non-Hermitian matrices occur as the final parameter estimation step in several blind source separation problems such as ACMA, JADE, PARAFAC, and SOBI. Previous approaches have been Jacobi iteration schemes and alternating projections. Here we show how the joint diagonalization problem can be formulated as a (weighted) subspace fitting problem so that it can be solved using the efficient Gauss-Newton optimization algorithm proposed for that problem. Since a good initial point is usually available, the algorithm converges very fast.