Hierarchy, priors and wavelets: structure and signal modelling using ICA

Authors:
Stephen Roberts;Evangelos Roussos;Rizwan Choudrey
Affiliations:
Pattern Analysis and Machine Learning Research Group, University of Oxford, Parks Road, Oxford OX1 3PJ, UK;Pattern Analysis and Machine Learning Research Group, University of Oxford, Parks Road, Oxford OX1 3PJ, UK;Pattern Analysis and Machine Learning Research Group, University of Oxford, Parks Road, Oxford OX1 3PJ, UK
Venue:
Signal Processing - Special issue on independent components analysis and beyond
Year:
2004

Citing 12
Cited 4

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
Independent component analysis, a new concept?

Signal Processing - Special issue on higher order statistics
An information-maximization approach to blind separation and blind deconvolution

Neural Computation
An introduction to variational methods for graphical models

Learning in graphical models
A view of the EM algorithm that justifies incremental, sparse, and other variants

Learning in graphical models
Independent factor analysis

Neural Computation
Independent component analysis: a flexible nonlinearity and decorrelating manifold approach

Neural Computation
Minimum-Entropy Data Partitioning Using Reversible Jump Markov Chain Monte Carlo

IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Independent Component Analysis: Principles and Practice

Independent Component Analysis: Principles and Practice
Bayesian parameter estimation via variational methods

Statistics and Computing
Fast and robust fixed-point algorithms for independent component analysis

IEEE Transactions on Neural Networks

Building Blocks for Variational Bayesian Learning of Latent Variable Models

The Journal of Machine Learning Research
Multivariate denoising using wavelets and principal component analysis

Computational Statistics & Data Analysis
Short Communication: Wavelet denoising using principal component analysis

Expert Systems with Applications: An International Journal
Bayesian independent component analysis with prior constraints: an application in biosignal analysis

Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

In many data analysis problems, it is useful to consider the data as generated from a set of unknown (latent) generators or sources. The observations we make of a system are then taken to be related to these sources through some unknown function. Furthermore, the (unknown) number of underlying latent sources may be less than the number of observations. Recent developments in independent component analysis (ICA) have shown that such data decomposition may be achieved in a mathematically elegant manner. In this paper, we extend the general ICA paradigm to include a very flexible source model, prior constraints and conditioning on sets of intermediate variables so that ICA forms one part of a hierarchical system. We show that such an approach allows for efficient unsupervised data partitioning and for sparse coding of signals using a hybrid wavelet-ICA model.