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
An Adaptive Method for Subband Decomposition ICA
Neural Computation
Variational learning for rectified factor analysis
Signal Processing
Hierarchical Feature Extraction for Compact Representation and Classification of Datasets
Neural Information Processing
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
Controlled complete ARMA independent process analysis
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Independent process analysis without a priori dimensional information
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Kernel-based nonlinear independent component analysis
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Post nonlinear independent subspace analysis
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Dependent component analysis for cosmology: a case study
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates
The Journal of Machine Learning Research
Tree-Dependent components of gene expression data for clustering
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Separation theorem for independent subspace analysis and its consequences
Pattern Recognition
A new approach to clustering and object detection with independent component analysis
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Clustering of signals using incomplete independent component analysis
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Region of interest based independent component analysis
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
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We present a generalization of independent component analysis (ICA), where instead of looking for a linear transform that makes the data components independent, we look for a transform that makes the data components well fit by a tree-structured graphical model. This tree-dependent component analysis (TCA) provides a tractable and flexible approach to weakening the assumption of independence in ICA. In particular, TCA allows the underlying graph to have multiple connected components, and thus the method is able to find "clusters" of components such that components are dependent within a cluster and independent between clusters. Finally, we make use of a notion of graphical models for time series due to Brillinger (1996) to extend these ideas to the temporal setting. In particular, we are able to fit models that incorporate tree-structured dependencies among multiple time series.