On gradient adaptation with unit-norm constraints

Authors:
S.C. Douglas;S. Amari;S.-Y. Kung
Affiliations:
Dept. of Electr. Eng., Southern Methodist Univ., Dallas, TX;-;-
Venue:
IEEE Transactions on Signal Processing
Year:
2000

Citing 0
Cited 11

Gradient Adaptive Algorithms for Contrast-Based Blind Deconvolution

Journal of VLSI Signal Processing Systems
Noisy input-output system identification approach for time delay estimation

Signal Processing
Gradient Adaptive Paraunitary Filter Banks for Spatio-Temporal Subspace Analysis and Multichannel Blind Deconvolution

Journal of VLSI Signal Processing Systems
Nonlinear Complex-Valued Extensions of Hebbian Learning: An Essay

Neural Computation
An adaptive constraint method for paraunitary filter banks with applications to spatiotemporal subspace tracking

EURASIP Journal on Applied Signal Processing
Fast communication: Adaptive multichannel blind identification using manifold optimization

Signal Processing
A Simple Adaptive Algorithm for Principle Component and Independent Component Analysis

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Fast communication: Gradient optimization p-norm-like constraint LMS algorithm for sparse system estimation

Signal Processing
Spherical mesh adaptive direct search for separating quasi-uncorrelated sources by range-based independent component analysis

Neural Computation
A sparse nonnegative matrix factorization technique for graph matching problems

Pattern Recognition
On the convergence of ICA algorithms with weighted orthogonal constraint

Digital Signal Processing

Quantified Score

Hi-index	35.68

Visualization

Abstract

In this correspondence, we describe gradient-based adaptive algorithms within parameter spaces that are specified by ||w||=1, where ||·|| is any vector norm. We provide several algorithm forms and relate them to true gradient procedures via their geometric structures. We also give algorithms that mitigate an inherent numerical instability for L2-norm-constrained optimization tasks. Simulations showing the performance of the techniques for independent component analysis are provided