Domijan, Katarina and Wilson, Simon P. (2009) Bayesian Kernel Projections for Classification of High Dimensional Data. Statistics and Computing, 21 (2). pp. 203-216. ISSN 0960-3174

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Abstract

A Bayesian multi-category kernel classication method is proposed. The hierarchical model is treated with a Bayesian inference procedure and the Gibbs sampler is implemented to find the posterior distributions of the parameters. The practical advantage of the full probabilistic model-based approach is that probability distributions of prediction can be obtained for new data points, which gives a more complete picture of classication. Large computational savings and improved classication performance can be achieved by a projection of the data to a subset of the principal axes of the feature space. The algorithm is aimed at high dimensional data sets where the dimension of measurements exceeds the number of observations. The applications considered in this paper are microarray, image processing and near-infrared spectroscopy data.

Item Type:	Article
Additional Information:	Preprint version of published article. The original publication is available at www.springerlink.com
Keywords:	Bayesian; Kernel Projections; Classication; High Dimensional Data;
Academic Unit:	Faculty of Science and Engineering > Mathematics and Statistics
Item ID:	2708
Identification Number:	https://doi.org/10.1007/s11222-009-9161-8
Depositing User:	Katarina Domijan
Date Deposited:	15 Sep 2011 08:17
Journal or Publication Title:	Statistics and Computing
Publisher:	Springer Verlag
Refereed:	No
URI:	http://www.springerlink.com/
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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