An algorithm for Ax = &lgr;Bx with symmetric and positive-definite A and B
SIAM Journal on Matrix Analysis and Applications
Globally and rapidly convergent algorithms for symmetric eigenproblems
SIAM Journal on Matrix Analysis and Applications
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Extraction of Visual Features for Lipreading
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient, high performance, subspace tracking for time-domain data
IEEE Transactions on Signal Processing
Bi-iteration SVD subspace tracking algorithms
IEEE Transactions on Signal Processing
RLS-based adaptive algorithms for generalized eigen-decomposition
IEEE Transactions on Signal Processing
Self-organizing algorithms for generalized eigen-decomposition
IEEE Transactions on Neural Networks
Computers and Electrical Engineering
Hi-index | 0.00 |
In this paper, we present an orthonormal version of the generalized signal subspace tracking. It is based on an interpretation of the generalized signal subspace as the solution of a constrained minimization task. This algorithm, referred to as the CGST algorithm, guarantees the C"x-orthonormality of the estimated generalized signal subspace basis at each iteration which C"x denotes the correlation matrix of the sequence x(t). An efficient implementation of the proposed algorithm enhances applicability of it in real time applications.