Fast Score Function Estimation with Application in ICA

Authors:
Nikos A. Vlassis
Affiliations:
-
Venue:
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Year:
2001

Citing 5
Cited 0

An information-maximization approach to blind separation and blind deconvolution

Neural Computation
Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources

Neural Computation
Independent factor analysis

Neural Computation
Blind source separation-semiparametric statistical approach

IEEE Transactions on Signal Processing
Efficient source adaptivity in independent component analysis

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

We describe an efficient method for accurate estimation of the score function of a random variable, which can be regarded as an extension of the FFT-based fast density estimation method of Silverman (1982), and which scales no more than linearly with the sample size. We demonstrate the utility of our approach in a real-life ICA problem involving the separation of eight sound signals, where better results are observed than using state-of-the-art ICA methods.