Independent component analysis based on marginal density estimation using weighted Parzen windows

Authors:
Jiann-Ming Wu;Meng-Hong Chen;Zheng-Han Lin
Affiliations:
Department of Applied Mathematics, National Dong Hwa University, Shoufeng, Hualien 971, Taiwan, ROC;Department of Applied Mathematics, National Dong Hwa University, Shoufeng, Hualien 971, Taiwan, ROC;Department of Applied Mathematics, National Dong Hwa University, Shoufeng, Hualien 971, Taiwan, ROC
Venue:
Neural Networks
Year:
2008

Citing 14
Cited 0

Introduction to information theory

Introduction to information theory
An information-maximization approach to blind separation and blind deconvolution

Neural Computation
Weighted Parzen Windows for Pattern Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-organization using Potts models

Neural Networks
A fast fixed-point algorithm for independent component analysis

Neural Computation
High-order contrasts for independent component analysis

Neural Computation
Independent factor analysis

Neural Computation
Blind separation of a mixture of uniformly distributed source signals: a novel approach

Neural Computation
Learning generative models of natural images

Neural Networks
Efficient greedy learning of Gaussian mixture models

Neural Computation
Probability density estimation from optimally condensed data samples

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast and robust fixed-point algorithms for independent component analysis

IEEE Transactions on Neural Networks
Independent component analysis using Potts models

IEEE Transactions on Neural Networks
Independent component analysis based on nonparametric density estimation

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This work proposes a novel algorithm for independent component analysis (ICA) based on marginal density estimation. The proposed ICA algorithm aims to search for an effective demixing matrix as well as weighted Parzen window (WPW) representations for marginal densities of independent components so as to express a factorial joint density for high dimensional observations. Following the linear mixture assumption, independent component analysis is mathematically translated to minimizing the Kullback-Leibler (KL) divergence of independent components. By using Potts encoding, we express the KL divergence in an approximating form, which is shown to be tractable with respect to the WPW parameters as well as the demixing matrix and can be minimized by two interactive dynamic modules derived by the annealed expectation-maximization method and the natural gradient descent method, respectively. By numerical simulations, we test the proposed ICA algorithm with observations separately sampled from linear mixtures of independent sources and real world signals, including fetal electrocardiograms, mixed facial images and event-related potentials, extensively showing its accuracy and reliability for independent component analysis in comparison with some other popular ICA algorithms.