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Flexible Bayesian Independent Component Analysis for Blind Source Separation
, 2001
"... Independent Component Analysis (ICA) is an important tool for extracting structure from data. ICA is traditionally performed under a maximum likelihood scheme in a latent variable model and in the absence of noise. Although extensively utilised, maximum likelihood estimation has well known drawbacks ..."
Abstract

Cited by 25 (4 self)
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Independent Component Analysis (ICA) is an important tool for extracting structure from data. ICA is traditionally performed under a maximum likelihood scheme in a latent variable model and in the absence of noise. Although extensively utilised, maximum likelihood estimation has well known drawbacks such as overfitting and sensitivity to localmaxima. In this paper, we propose a Bayesian learning scheme using the variational paradigm to learn the parameters of the model, estimate the source densities, and  together with Automatic Relevance Determination (ARD)  to infer the number of latent dimensions. We illustrate our method by separating a noisy mixture of images, estimating the noise and correctly inferring the true number of sources.
An ensemble learning approach to independent component analysis
 Proc. of Neural Networks for Signal Processing
, 2000
"... ..."
Modeling Cellular Processes with Variational Bayesian Cooperative Vector Quantizer
 In Proceedings of Pacific Symposium on Biocomputing
, 2004
"... Gene expression of a cell is controlled by sophisticated cellular processes. ..."
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Cited by 3 (3 self)
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Gene expression of a cell is controlled by sophisticated cellular processes.
Variational Bayesian learning of cooperative vector quantizer model – theory
, 2002
"... This is the first part of a twoparted report on development of a statistical learning algorithm for a latent variable model referred to as cooperative vector quantizer model. This part presents the theory and mathematical derivations of a variational Bayesian learning algorithm for the model. The m ..."
Abstract

Cited by 2 (2 self)
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This is the first part of a twoparted report on development of a statistical learning algorithm for a latent variable model referred to as cooperative vector quantizer model. This part presents the theory and mathematical derivations of a variational Bayesian learning algorithm for the model. The model has general applications in the field of machine learning and signal processing. For example it can be used to solve the problem of blind source separation or image separation. Our special interest is in its potential biological application in that we can use the model to simulate signal transduction components regulating gene expression as latent variables. The algorithm is capable of automatically and efficiently determining the number of latent variables of the model, estimating the distribution of the parameters and latent variables. Thus, we can use the model to address following biological questions regarding gene expression regulation: (1) What are the key signal transduction components regulating gene expression in a given kind of cell; (2) How many key components are needed to efficiently encode information for gene expression regulation; (3) What are the states of the key components for a given gene expression data point. Such information will provide insight for understanding the mechanism of information organization of cells, mechanism of diseases and drug effect/toxicity. 2
Infinite Independent Components Analysis by
"... I hereby declare that, except where specifically indicated, the work submitted herein is my own original work. ..."
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I hereby declare that, except where specifically indicated, the work submitted herein is my own original work.
Pacific Symposium on Biocomputing 9:533544(2004) MODELING CELLULAR PROCESSES WITH VARIATIONAL BAYESIAN COOPERATIVE VECTOR QUANTIZER
"... Gene expression of a cell is controlled by sophisticated cellular processes. The capability of inferring the states of these cellular processes would provide insight into the mechanism of gene expression control system. In this paper, we propose and investigate the cooperative vector quantizer (CVQ) ..."
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Gene expression of a cell is controlled by sophisticated cellular processes. The capability of inferring the states of these cellular processes would provide insight into the mechanism of gene expression control system. In this paper, we propose and investigate the cooperative vector quantizer (CVQ) model for analysis of microarray data. The CVQ model could be capable of decomposing observed microarray data into many different regulatory subprocesses. To make the CVQ analysis tractable we develop and apply variational approximations. Bayesian model selection is employed in the model, so that the optimal number processes is determined purely from observed microarray data. We test the model and algorithms on two datasets: (1) simulated geneexpression data and (2) realworld yeast cellcycle microarray data. The results illustrate the ability of the CVQ approach to recover and characterize regulatory gene expression subprocesses, indicating a potential for advanced gene expression data analysis. 1