Adaptive filter theory
Adaptive algorithms and stochastic approximations
Adaptive algorithms and stochastic approximations
SVD and signal processing: algorithms, applications and architectures
SVD and signal processing: algorithms, applications and architectures
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Time Series Segmentation Using a Novel Adaptive Eigendecomposition Algorithm
Journal of VLSI Signal Processing Systems
A quasi-Newton adaptive algorithm for generalized symmetriceigenvalue problem
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Self-organizing algorithms for generalized eigen-decomposition
IEEE Transactions on Neural Networks
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
Fast adaptive LDA using quasi-Newton algorithm
Pattern Recognition Letters
Robust adaptive modified Newton algorithm for generalized eigendecomposition and its application
EURASIP Journal on Advances in Signal Processing
Joint blind source separation by multiset canonical correlation analysis
IEEE Transactions on Signal Processing
A neural network based data least squares algorithm for channel equalization
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Application of spatio-temporal filtering to fetal electrocardiogram enhancement
Computer Methods and Programs in Biomedicine
A new incremental optimal feature extraction method for on-line applications
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Multidimensional Systems and Signal Processing
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Generalized eigendecomposition (GED) plays a vital role in many signal-processing applications. In this paper, we will propose a new method for computing the generalized eigenvectors, which is on-line and resembles the RLS algorithm for Wiener filtering. We further present a proof to show convergence to the exact solution and simulations have shown that the algorithm is faster than most of the traditional methods. This algorithm belongs to the class of fixed-point algorithms and hence does not require any external step-size parameters like the gradient-based methods. Simulations are performed on synthetic data and compared with other algorithms found in literature. Finally we will demonstrate the application of GED in the design of a CDMA receiver for direct-sequence spread spectrum signals.