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Bayesian block-diagonal predictive classifier for Gaussian data
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.ORCID iD: 0000-0003-1489-8512
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2013 (English)In: Synergies of Soft Computing and Statistics for Intelligent Data Analysis, Springer, 2013, 543-551 p.Conference paper (Refereed)
Abstract [en]

The paper presents a method for constructing Bayesian predictive classifier in a high-dimensional setting. Given that classes are represented by Gaussian distributions with block-structured covariance matrix, a closed form expression for the posterior predictive distribution of the data is established. Due to factorization of this distribution, the resulting Bayesian predictive and marginal classifier provides an efficient solution to the high-dimensional problem by splitting it into smaller tractable problems. In a simulation study we show that the suggested classifier outperforms several alternative algorithms such as linear discriminant analysis based on block-wise inverse covariance estimators and the shrunken centroids regularized discriminant analysis.

Place, publisher, year, edition, pages
Springer, 2013. 543-551 p.
, Advances in Intelligent Systems and Computing, ISSN 2194-5357 ; 190 AISC
Keyword [en]
Covariance estimators, discriminant analysis, high-dimensional data, hyperparameters
National Category
Computer and Information Science Mathematics
URN: urn:nbn:se:kth:diva-117792DOI: 10.1007/978-3-642-33042-1_58ISI: 000312969600058ScopusID: 2-s2.0-84870759465ISBN: 978-364233041-4OAI: diva2:603127
6th International Conference on Soft Methods in Probability and Statistics, SMPS 2012, 4 October 2012 through 6 October 2012, Konstanz

QC 20130205

Available from: 2013-02-05 Created: 2013-02-05 Last updated: 2013-04-16Bibliographically approved

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