The Problem of Overlearning in High-Order ICA Approaches: Analysis and Solutions

Authors:
Jaakko Särelä;Ricardo Vigário
Affiliations:
-;-
Venue:
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Year:
2001

Citing 4
Cited 1

Blind separation of sources, Part 1: an adaptive algorithm based on neuromimetic architecture

Signal Processing
Independent component analysis, a new concept?

Signal Processing - Special issue on higher order statistics
Independent component analysis for identification of artifacts in magnetoencephalographic recordings

NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Data-adaptive wavelets and multi-scale singular-spectrum analysis

Physica D

Blind decomposition of multimodal evoked responses and DC fields

Exploratory analysis and data modeling in functional neuroimaging

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider a type of overlearning typical of independent component analysis algorithms. These can be seen to minimize the mutual information between source estimates. The overlearning causes spikelike signals if there are too few samples or there is a considerable amount of noise present. It is argued that if the data has flicker noise the problem is more severe and is better characterized by bumps instead of spikes. The problem is demonstrated using recorded magnetoencephalographic signals. Several methods are suggested that attempt to solve the overlearning problem or, at least, diminishlreduce its effects.