论文标题
使用高斯工艺先验的贝叶斯回归和分类，概率密度函数索引
Bayesian Regression and Classification Using Gaussian Process Priors Indexed by Probability Density Functions
论文作者
论文摘要
在本文中，我们介绍了由概率密度函数索引的高斯过程的概念，用于扩展Matérn的协方差函数家族。我们使用信息几何形状中的一些工具来提高贝叶斯学习模型的效率和计算方面。我们特别展示了如何将具有高斯过程的贝叶斯推断（协方差参数估计和预测）在概率密度函数的空间上作用。我们的框架具有分类和推断在非线性子空间上的数据观测值的能力。与当前最新方法相比，对多种合成，半合成和实际数据进行了广泛的实验，证明了所提出方法的有效性和效率。
In this paper, we introduce the notion of Gaussian processes indexed by probability density functions for extending the Matérn family of covariance functions. We use some tools from information geometry to improve the efficiency and the computational aspects of the Bayesian learning model. We particularly show how a Bayesian inference with a Gaussian process prior (covariance parameters estimation and prediction) can be put into action on the space of probability density functions. Our framework has the capacity of classifiying and infering on data observations that lie on nonlinear subspaces. Extensive experiments on multiple synthetic, semi-synthetic and real data demonstrate the effectiveness and the efficiency of the proposed methods in comparison with current state-of-the-art methods.
