Peer-Reviewed Journal Details
Mandatory Fields
Domijan, K;Wilson, SP
2011
April
Statistics and Computing
Bayesian kernel projections for classification of high dimensional data
Published
3 ()
Optional Fields
SUPPORT VECTOR MACHINES ADAPTIVE SPARSENESS VARIABLE SELECTION GAUSSIAN-PROCESSES REGRESSION CLASSIFIERS PREDICTION ARTICLE BROWNE CHOICE
21
203
216
A Bayesian multi-category kernel classification method is proposed. The algorithm performs the classification of the projections of the data to the principal axes of the feature space. The advantage of this approach is that the regression coefficients are identifiable and sparse, leading to large computational savings and improved classification performance. The degree of sparsity is regulated in a novel framework based on Bayesian decision theory. The Gibbs sampler is implemented to find the posterior distributions of the parameters, thus probability distributions of prediction can be obtained for new data points, which gives a more complete picture of classification. 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.
DORDRECHT
0960-3174
10.1007/s11222-009-9161-8
Grant Details