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23
Infinite Sparse Factor Analysis and Infinite Independent Components Analysis
"... Abstract. A nonparametric Bayesian extension of Independent Components Analysis (ICA) is proposed where observed data Y is modelled as a linear superposition, G, of a potentially infinite number of hidden sources, X. Whether a given source is active for a specific data point is specified by an infin ..."
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Abstract. A nonparametric Bayesian extension of Independent Components Analysis (ICA) is proposed where observed data Y is modelled as a linear superposition, G, of a potentially infinite number of hidden sources, X. Whether a given source is active for a specific data point is specified by an infinite binary matrix, Z. The resulting sparse representation allows increased data reduction compared to standard ICA. We define a prior on Z using the Indian Buffet Process (IBP). We describe four variants of the model, with Gaussian or Laplacian priors on X and the one or twoparameter IBPs. We demonstrate Bayesian inference under these models using a Markov Chain Monte Carlo (MCMC) algorithm on synthetic and gene expression data and compare to standard ICA algorithms. 1
Biologically valid linear factor models of gene expression
 Bioinformatics
, 2004
"... Motivation The identification of physiological processes underlying and generating the expression pattern observed in microarray experiments is a major challenge. Principal Component Analysis (PCA) is a linear multivariate statistical method that is regularly employed for that purpose as it provides ..."
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Cited by 23 (1 self)
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Motivation The identification of physiological processes underlying and generating the expression pattern observed in microarray experiments is a major challenge. Principal Component Analysis (PCA) is a linear multivariate statistical method that is regularly employed for that purpose as it provides a reduceddimensional representation for subsequent study of possible biological processes responding to the particular experimental conditions. Making explicit the data assumptions underlying PCA highlights their lack of biological validity thus making biological interpretation of the principal components problematic. A microarray data representation which enables clear biological interpretation is a desirable analysis tool. Results We address this issue by employing the probabilistic interpretation of Principal Component Analysis and proposing alternative Linear Factor Models which are based on refined biological assumptions. A practical study on two wellunderstood microarray data sets highlights the weakness of Principal Component Analysis and the greater biological interpretability of the linear models we have developed. Availability The model estimation routines are currently implemented as Matlab routines and these, as well as data and results reported, are available from the following URL
Metabolite fingerprinting: detecting biological features by independent component analysis
 Bioinformatics
, 2004
"... features by independent component analysis ..."
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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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Gene expression of a cell is controlled by sophisticated cellular processes.
Independent component analysis of starch deficient pgm mutants
 In Giegerich,R. and Stoye,J. (eds), Proc. of the German Conference on Bioinformatics 2004. Gesellschaft f ür Informatik
, 2004
"... Abstract: Changes in enzymatic activities in response to carbon starvation were investigated in Arabidopsis thaliana in two distinct experiments. One compares the Columbia ecotype (Col0) and its starch deficient pgm mutant (plastidial phosphoglucomutase), the other investigates the enzymatic activi ..."
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Cited by 2 (0 self)
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Abstract: Changes in enzymatic activities in response to carbon starvation were investigated in Arabidopsis thaliana in two distinct experiments. One compares the Columbia ecotype (Col0) and its starch deficient pgm mutant (plastidial phosphoglucomutase), the other investigates the enzymatic activities of Col0 under extended night conditions. A classical technique for detecting and visualizing relevant information from the measured data is principal component analysis (PCA). We show that independent component analysis (ICA) is more suitable for our questions and the results are more precise than those obtained with PCA. This higher informative power is only achieved when ICA is combined with suitable preprocessing and evaluation criteria. It is essential to first reduce the dimensionality of the data set, using PCA. The number of principal components determines the quality of ICA significantly, therefore we propose a criterion for estimating the optimal dimension automatically. The measure of kurtosis is used to sort the extracted components. We found that ICA could detect on the one hand the time component of the extended night experiment, and on the other hand a discriminating component in the pgm mutant experiment. In both components the most important enzymes were the same, confirming the carbon starvation phenotype in the mutant.
GEOMETRIC OPTIMIZATION METHODS FOR INDEPENDENT COMPONENT ANALYSIS APPLIED ON GENE EXPRESSION DATA
"... DNA microarrays provide a huge amount of data and require therefore dimensionality reduction methods to extract meaningful biological information. Independent Component Analysis (ICA) was proposed by several authors as an interesting means. Unfortunately, experimental data are usually of poor qualit ..."
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DNA microarrays provide a huge amount of data and require therefore dimensionality reduction methods to extract meaningful biological information. Independent Component Analysis (ICA) was proposed by several authors as an interesting means. Unfortunately, experimental data are usually of poor quality because of noise, outliers and lack of samples. Robustness to these hurdles will thus be a key feature for an ICA algorithm. This paper identi�es a robust contrast function and proposes a new ICA algorithm. Index Terms — Independent Component Analysis (ICA), 1.
Independent Arrays or Independent Time Courses for Gene Expression Time Series
"... Abstract — In this paper we apply three different independent component analysis (ICA) methods, including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA), to gene expression time series data and compare their performance in clustering genes and in finding biologically meaning ..."
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Abstract — In this paper we apply three different independent component analysis (ICA) methods, including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA), to gene expression time series data and compare their performance in clustering genes and in finding biologically meaningful modes. Only spatial ICA was applied to gene expression data [3], [4]. However, in the case of yeast cell cyclerelated gene expression time series data, our comparative study reveals that tICA outperforms sICA and stICA in the task of gene clustering and stICA finds linear modes that best match the cell cycle. I.
Date:....................... Signed:..............................................
, 2004
"... I hereby declare that my dissertation entitled \Variational Message Passing and its Applications " is not substantially the same as any that I have submitted for a degree or diploma or other qualication at any other university. I further state that no part of my dissertation has already been o ..."
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I hereby declare that my dissertation entitled \Variational Message Passing and its Applications " is not substantially the same as any that I have submitted for a degree or diploma or other qualication at any other university. I further state that no part of my dissertation has already been or is being concurrently submitted for any such degree or diploma or other qualication. Except where explicit reference is made to the work of others, this dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration. This dissertation does not exceed sixty thousand words in length.
Title: Extrinsic Regularization in Parameter Optimization for Support Vector Machines
, 2006
"... The undersigned hereby certify that they have read and recommend to the Faculty of Graduate Studies for acceptance a thesis entitled “Extrinsic Regularization in Parameter Optimization for Support Vector Machines ” by Matthew D. Boardman ..."
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The undersigned hereby certify that they have read and recommend to the Faculty of Graduate Studies for acceptance a thesis entitled “Extrinsic Regularization in Parameter Optimization for Support Vector Machines ” by Matthew D. Boardman