Simultaneous Predictive Gaussian Classifiers

A1 Journal article (refereed)


Internal Authors/Editors


Publication Details

List of Authors: Cui YQ, Siren J, Koski T, Corander J
Publisher: SPRINGER
Publication year: 2016
Journal: Journal of Classification
Journal acronym: J CLASSIF
Volume number: 33
Issue number: 1
Start page: 73
End page: 102
Number of pages: 30
ISSN: 0176-4268
eISSN: 1432-1343


Abstract

Gaussian distribution has for several decades been ubiquitous in the theory and practice of statistical classification. Despite the early proposals motivating the use of predictive inference to design a classifier, this approach has gained relatively little attention apart from certain specific applications, such as speech recognition where its optimality has been widely acknowledged. Here we examine statistical properties of different inductive classification rules under a generic Gaussian model and demonstrate the optimality of considering simultaneous classification of multiple samples under an attractive loss function. It is shown that the simpler independent classification of samples leads asymptotically to the same optimal rule as the simultaneous classifier when the amount of training data increases, if the dimensionality of the feature space is bounded in an appropriate manner. Numerical investigations suggest that the simultaneous predictive classifier can lead to higher classification accuracy than the independent rule in the low-dimensional case, whereas the simultaneous approach suffers more from noise when the dimensionality increases.


Keywords

Bayesian modeling, Discriminant analysis, Inductive learning, Predictive inference, Probabilistic classification

Last updated on 2019-16-10 at 02:33