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Bayesian kernel projections for classification of high dimensional data

机译：贝叶斯核投影用于高维数据分类

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摘要

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.
机译：提出了一种贝叶斯多类别核分类方法。该算法将数据投影到特征空间的主轴上进行分类。这种方法的优点是回归系数可识别且稀疏，从而节省了大量计算量并提高了分类性能。稀疏程度在基于贝叶斯决策理论的新颖框架中进行调节。吉布斯采样器的实现是为了找到参数的后验分布，从而可以获得新数据点的预测概率分布，从而给出更完整的分类图。该算法针对测量维度超过观察次数的高维数据集。本文考虑的应用是微阵列，图像处理和近红外光谱数据。

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