Time series: theory and methods
Time series: theory and methods
Introduction to algorithms
Elements of information theory
Elements of information theory
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
High-order contrasts for independent component analysis
Neural Computation
Learning Bayesian networks with local structure
Learning in graphical models
Neural Computation
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Introduction to Linear Optimization
Introduction to Linear Optimization
Kernel independent component analysis
The Journal of Machine Learning Research
Topographic Independent Component Analysis
Neural Computation
A Constrained EM Algorithm for Independent Component Analysis
Neural Computation
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixture of sources via anindependent component analysis
IEEE Transactions on Signal Processing
Mutual information approach to blind separation of stationary sources
IEEE Transactions on Information Theory
Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces
The Journal of Machine Learning Research
A unifying model for blind separation of independent sources
Signal Processing
Undercomplete Blind Subspace Deconvolution
The Journal of Machine Learning Research
Clustering of dependent components: a new paradigm for fMRI signal detection
EURASIP Journal on Applied Signal Processing
ICA and ISA using Schweizer-Wolff measure of dependence
Proceedings of the 25th international conference on Machine learning
Learning graphical models for hypothesis testing and classification
IEEE Transactions on Signal Processing
The Journal of Machine Learning Research
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We present a generalization of independent component analysis(ICA), where instead of looking for a linear transform that makesthe data components independent, we look for a transform that makesthe data components well fit by a tree-structured graphical model.This tree-dependent component analysis (TCA) provides atractable and flexible approach to weakening the assumption ofindependence in ICA. In particular, TCA allows the underlying graphto have multiple connected components, and thus the method is ableto find "clusters" of components such that components are dependentwithin a cluster and independent between clusters. Finally, we makeuse of a notion of graphical models for time series due toBrillinger (1996) to extend these ideas to the temporal setting. Inparticular, we are able to fit models that incorporatetree-structured dependencies among multiple time series.