Bayes PCA Revisited
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Bayes PCA Revisited. / Sporring, Jon.
Department of Computer Science : Museum Tusculanum, 2008. 12 p. (Department of Computer Science. University of Copenhagen. Technical Report; No. 08-09).Research output: Book/Report › Report › Research
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TY - RPRT
T1 - Bayes PCA Revisited
AU - Sporring, Jon
PY - 2008
Y1 - 2008
N2 - Principle Component Analysis is a simple tool to obtain linear models forstochastic data and is used both for a data reduction or equivalently noise elim-ination and for data analysis. Principle Component Analysis ts a multivariateGaussian distribution to the data, and the typical method is by using the log-likelihood estimator. However for small sets of high dimensional data, the log-likelihood estimator is often far from convergence, and therefore reliable modelsmust be obtained by use of prior information. In this paper, we will examinean earlier work on reconstructing missing data using statistical knowledge andregularization, we will show the circumstances for which this is equivalent toa Bayes estimation, we will give an expository presentation of Bayes PrincipleComponent Analysis for a range of exponential type priors, and we will developalgorithms for their estimate.
AB - Principle Component Analysis is a simple tool to obtain linear models forstochastic data and is used both for a data reduction or equivalently noise elim-ination and for data analysis. Principle Component Analysis ts a multivariateGaussian distribution to the data, and the typical method is by using the log-likelihood estimator. However for small sets of high dimensional data, the log-likelihood estimator is often far from convergence, and therefore reliable modelsmust be obtained by use of prior information. In this paper, we will examinean earlier work on reconstructing missing data using statistical knowledge andregularization, we will show the circumstances for which this is equivalent toa Bayes estimation, we will give an expository presentation of Bayes PrincipleComponent Analysis for a range of exponential type priors, and we will developalgorithms for their estimate.
M3 - Report
T3 - Department of Computer Science. University of Copenhagen. Technical Report
BT - Bayes PCA Revisited
PB - Museum Tusculanum
CY - Department of Computer Science
ER -
ID: 5503190