Bayesian kernel projections for classification of high dimensional data |
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Authors: | Katarina Domijan Simon P Wilson |
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Institution: | 1.Mathematics Department,NUI Maynooth,Maynooth,Ireland;2.School of Computer Science,Trinity College Dublin,Dublin,Ireland |
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Abstract: | 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. |
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Keywords: | |
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