Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Beyond independent components: trees and clusters
The Journal of Machine Learning Research
Biologically valid linear factor models of gene expression
Bioinformatics
On variations of power iteration
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Topographic independent component analysis of gene expression time series data
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Controlled complete ARMA independent process analysis
IJCNN'09 Proceedings of the 2009 international joint conference on 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
Separation theorem for independent subspace analysis and its consequences
Pattern Recognition
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Tree-dependent component analysis (TCA) is a generalization of independent component analysis (ICA), the goal of which is to model the multivariate data by a linear transformation of latent variables, while latent variables fit by a tree-structured graphical model. In contrast to ICA, TCA allows dependent structure of latent variables and also consider non-spanning trees (forests). In this paper, we present a TCA-based method of clustering gene expression data. Empirical study with yeast cell cycle-related data, yeast metabolic shift data, and yeast sporulation data, shows that TCA is more suitable for gene clustering, compared to principal component analysis (PCA) as well as ICA.