Pavel Laskov

Publications of Pavel Laskov

• Journal of Machine Learning Research 5 (2004) 777--800 Submitted 12/03; Revised 5/04; Published 7/04 A Fast Algorithm for Joint Diagonalization with Non-orthogonal

  Authors: Andreas Ziehe, Pavel Laskov, Kekul Estrasse, Guido Nolte

  10/2004;

  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 addressesA 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.

• A Fast Algorithm for Joint Diagonalization with Non-orthogonal Transformations and its Application to Blind Source Separation.

  Authors: Andreas Ziehe, Pavel Laskov, Guido Nolte, Klaus-Robert Müller

  Journal of Machine Learning Research. 01/2004; 5:777-800.