Space or time adaptive signal processing by neural network models
AIP Conference Proceedings 151 on Neural Networks for Computing
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Connectionist learning procedures
Artificial Intelligence
Keeping the neural networks simple by minimizing the description length of the weights
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
An introduction to variational methods for graphical models
Learning in graphical models
Mixtures of probabilistic principal component analyzers
Neural Computation
Neural Computation
Restructuring sparse high dimensional data for effective retrieval
Proceedings of the 1998 conference on Advances in neural information processing systems II
SMEM algorithm for mixture models
Proceedings of the 1998 conference on Advances in neural information processing systems II
IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimum-Entropy Data Partitioning Using Reversible Jump Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Independent Component Analysis: Principles and Practice
Independent Component Analysis: Principles and Practice
Bayesian parameter estimation via variational methods
Statistics and Computing
Inferring parameters and structure of latent variable models by variational bayes
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Blind separation of nonlinear mixtures by variational Bayesian learning
Digital Signal Processing
Watermarking Security Incorporating Natural Scene Statistics
Information Hiding
ICA Mixture Modeling for the Classification of Materials in Impact-Echo Testing
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
A general procedure for learning mixtures of independent component analyzers
Pattern Recognition
Image similarity based on hierarchies of ICA mixtures
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
IEEE Transactions on Image Processing
ICA mixtures applied to ultrasonic nondestructive classification of archaeological ceramics
EURASIP Journal on Advances in Signal Processing - Special issue on signal processing in advanced nondestructive materials inspection
Super-Gaussian mixture source model for ICA
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
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
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There has been growing interest in subspace data modeling over the past few years. Methods such as principal component analysis, factor analysis, and independent component analysis have gained in popularity and have found many applications in image modeling, signal processing, and data compression, to name just a few. As applications and computing power grow, more and more sophisticated analyses and meaningful representations are sought. Mixture modeling methods have been proposed for principal and factor analyzers that exploit local gaussian features in the subspace manifolds. Meaningful representations may be lost, however, if these local features are nongaussian or discontinuous. In this article, we propose extending the gaussian analyzers mixture model to an independent component analyzers mixture model. We employ recent developments in variational Bayesian inference and structure determination to construct a novel approach for modeling nongaussian, discontinuous manifolds. We automatically determine the local dimensionality of each manifold and use variational inference to calculate the optimum number of ICA components needed in our mixture model. We demonstrate our framework on complex synthetic data and illustrate its application to real data by decomposing functional magnetic resonance images into meaningful--and medically useful--features.