论文标题
分类的高斯流程
Skew Gaussian Processes for Classification
论文作者
论文摘要
高斯流程（GPS）是功能上的分布，它为回归和分类提供了贝叶斯非参数方法。尽管他们成功了，但GP在某些应用中的使用有限，例如，在某些情况下，与其平均值相称的分布是一个不合理的模型。例如，这意味着平均值和中位数重合，而不对称（偏斜）分布中的平均值和中位数可能是不同的数字。在本文中，我们提出偏斜过程（skewgps）作为非参数的先验功能。 skeWGP将多元统一的偏度正常分布扩展到有限尺寸向量上，以扩展到随机过程。 SkeWGP类别的分布类别包括GPS，因此SkeWGPS继承了GPS的所有良好特性，并通过允许在概率模型中不对称来提高其灵活性。通过利用偏斜和概率可能性是共轭模型的事实，我们为这种新的非参数分类器的边际可能性和预测分布提供了封闭形式的表达式。我们从经验上验证，所提出的SkeWGP分类器比基于Laplace的方法或期望传播的GP分类器提供了更好的性能。
Gaussian processes (GPs) are distributions over functions, which provide a Bayesian nonparametric approach to regression and classification. In spite of their success, GPs have limited use in some applications, for example, in some cases a symmetric distribution with respect to its mean is an unreasonable model. This implies, for instance, that the mean and the median coincide, while the mean and median in an asymmetric (skewed) distribution can be different numbers. In this paper, we propose Skew-Gaussian processes (SkewGPs) as a non-parametric prior over functions. A SkewGP extends the multivariate Unified Skew-Normal distribution over finite dimensional vectors to a stochastic processes. The SkewGP class of distributions includes GPs and, therefore, SkewGPs inherit all good properties of GPs and increase their flexibility by allowing asymmetry in the probabilistic model. By exploiting the fact that SkewGP and probit likelihood are conjugate model, we derive closed form expressions for the marginal likelihood and predictive distribution of this new nonparametric classifier. We verify empirically that the proposed SkewGP classifier provides a better performance than a GP classifier based on either Laplace's method or Expectation Propagation.
