Jacobi Angles for Simultaneous Diagonalization
SIAM Journal on Matrix Analysis and Applications
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Joint Approximate Diagonalization of Positive Definite Hermitian Matrices
SIAM Journal on Matrix Analysis and Applications
The Journal of Machine Learning Research
SIAM Journal on Matrix Analysis and Applications
Joint diagonalization via subspace fitting techniques
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
An analytical constant modulus algorithm
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
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In this paper we consider the problem of finding approximate common rank one factors for a set of matrices. Instead of jointly diagonalizing the matrices, we perform calculations directly in the problem intrinsic domain: we present an algorithm, AROFAC, which searches the approximate linear span of the matrices using an indicator function for the rank one factors, finding specific single sources. We evaluate the feasibility of this approach by discussing simulations on generated data and a neurophysiological dataset. Note however that our contribution is intended to be mainly conceptual in nature.