论文标题
高斯判别物的最佳预测
Optimal Projections for Gaussian Discriminants
论文作者
论文摘要
研究了通过高斯类密度进行判别分析的最佳预测的问题。与大多数现有问题的方法不同，优化的重点是基于后验概率估计值的多项式可能性，该估计直接捕获了类的可区分性。除了在这种情况下，除了分类的情况下，还考虑了无监督的聚类对应物。查找最佳预测提供了降低和正则化的实用程序，以及具有指导性的可视化，以提供更好的模型解释性。拟议方法的实际应用显示出对分类和聚类的巨大希望。实现该方法的代码可从https://github.com/davidhofmeyr/opgd的r软件包的形式获得。
The problem of obtaining optimal projections for performing discriminant analysis with Gaussian class densities is studied. Unlike in most existing approaches to the problem, the focus of the optimisation is on the multinomial likelihood based on posterior probability estimates, which directly captures discriminability of classes. In addition to the more commonly considered problem, in this context, of classification, the unsupervised clustering counterpart is also considered. Finding optimal projections offers utility for dimension reduction and regularisation, as well as instructive visualisation for better model interpretability. Practical applications of the proposed approach show considerable promise for both classification and clustering. Code to implement the proposed method is available in the form of an R package from https://github.com/DavidHofmeyr/OPGD.
