Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Fast RLS-Like Algorithm for Generalized Eigendecomposition and its Applications
Journal of VLSI Signal Processing Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Projection approximation subspace tracking
IEEE Transactions on Signal Processing
A quasi-Newton adaptive algorithm for generalized symmetriceigenvalue problem
IEEE Transactions on Signal Processing
RLS-based adaptive algorithms for generalized eigen-decomposition
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Self-organizing algorithms for generalized eigen-decomposition
IEEE Transactions on Neural Networks
Face recognition using LDA-based algorithms
IEEE Transactions on Neural Networks
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We propose a robust adaptive algorithm for generalized eigendecomposition problems that arise in modern signal processing applications. To that extent, the generalized eigendecomposition problem is reinterpreted as an unconstrained nonlinear optimization problem. Starting from the proposed cost function and making use of an approximation of the Hessian matrix, a robust modified Newton algorithm is derived. A rigorous analysis of its convergence properties is presented by using stochastic approximation theory. We also apply this theory to solve the signal reception problem of multicarrier DS-CDMA to illustrate its practical application. The simulation results show that the proposed algorithm has fast convergence and excellent tracking capability, which are important in a practical time-varying communication environment.