Bayesian Block-Diagonal Predictive Classifier for Gaussian Data
2013 (English)In: Synergies of Soft Computing and Statistics for Intelligent Data Analysis / [ed] Rudolf Kruse, Michael R. Berthold, Christian Moewes, María Ángeles Gil, Przemysław Grzegorzewski, Olgierd Hryniewicz, Springer Berlin/Heidelberg, 2013, 543-551 p.Chapter in book (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 Berlin/Heidelberg, 2013. 543-551 p.
, Advances in Intelligent Systems and Computing, ISSN 2194-5357 ; 190
Social Sciences Engineering and Technology
Research subject Statistics
IdentifiersURN: urn:nbn:se:su:diva-95262DOI: 10.1007/978-3-642-33042-1ISBN: 978-3-642-33041-4OAI: oai:DiVA.org:su-95262DiVA: diva2:659219