Bayesian block-diagonal predictive classifier for Gaussian data
2013 (English)In: Synergies of Soft Computing and Statistics for Intelligent Data Analysis, Springer, 2013, 543-551 p.Conference paper (Refereed)
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
Covariance estimators, discriminant analysis, high-dimensional data, hyperparameters
Computer and Information Science Mathematics
IdentifiersURN: urn:nbn:se:kth:diva-117792DOI: 10.1007/978-3-642-33042-1_58ISI: 000312969600058ScopusID: 2-s2.0-84870759465ISBN: 978-364233041-4OAI: oai:DiVA.org:kth-117792DiVA: diva2:603127
6th International Conference on Soft Methods in Probability and Statistics, SMPS 2012, 4 October 2012 through 6 October 2012, Konstanz
QC 201302052013-02-052013-02-052013-04-16Bibliographically approved