Learning independent components on the orthogonal group of matrices by retractions

Authors:
Simone Fiori
Affiliations:
Dipartimento di Elettronica, Intelligenza Artificiale e Telecomunicazioni (DEIT), Università Politecnica delle Marche, Ancona, Italy 60131
Venue:
Neural Processing Letters
Year:
2007

Citing 12
Cited 1

A Class of Intrinsic Schemes for Orthogonal Integration

SIAM Journal on Numerical Analysis
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Optimal Linear Representations of Images for Object Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural learning by geometric integration of reduced 'rigid-body' equations

Journal of Computational and Applied Mathematics
Quasi-Geodesic Neural Learning Algorithms Over the Orthogonal Group: A Tutorial

The Journal of Machine Learning Research
Fixed-point neural independent component analysis algorithms on the orthogonal group

Future Generation Computer Systems
Editorial: Geometrical methods in neural networks and learning

Neurocomputing
Learning algorithms utilizing quasi-geodesic flows on the Stiefel manifold

Neurocomputing
A theory for learning based on rigid bodies dynamics

IEEE Transactions on Neural Networks
Algorithms for nonnegative independent component analysis

IEEE Transactions on Neural Networks
Monotonic convergence of fixed-point algorithms for ICA

IEEE Transactions on Neural Networks
Fast fixed-point neural blind-deconvolution algorithm

IEEE Transactions on Neural Networks

An algorithm to compute averages on matrix Lie groups

IEEE Transactions on Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Neural independent component learning algorithms based on optimization on manifolds have attracted interest in the neural network community. In the past years, we have developed learning algorithms specialized for the orthogonal group of matrices as parameters manifold. Here, we sketch a view of these algorithms by the help of `retractions' on manifolds.