论文标题
有效转化的高斯流程，用于非平稳依赖的多级分类
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification
论文作者
论文摘要
这项工作介绍了有效变化的高斯过程（ETGP），这是一种创建C随机过程的新方法，其特征是：1）C过程是非静止的，2）C过程取决于构造，而无需进行混合矩阵，而无需进行混合训练和做出预测，因为covar opportions（gp）的操作非常有效（gp），并且指标依赖（gp），并且指向MANIDERS（gp），并且指定性地指标。关于过程的数量。这使得ETGP特别适用于大量课程的多级问题，这是这项工作中研究的问题。 ETGP利用了最近提出的转换的高斯过程（TGP），这是一种通过使用可逆转换转换高斯过程指定的随机过程。但是，与TGP不同，ETGP是通过使用C逆转转化从GP转换单个样本来构建的。我们为提出的模型提供了有效的稀疏变异推理算法，并在5个分类任务中演示了其实用性，其中包括低/中/大数据集和不同数量的类别，范围从几个到数百。我们的结果表明，通常，基于GP的多级分类的ETGP胜过最先进的方法，并且计算成本较低（大约一个数量级较小）。
This work introduces the Efficient Transformed Gaussian Process (ETGP), a new way of creating C stochastic processes characterized by: 1) the C processes are non-stationary, 2) the C processes are dependent by construction without needing a mixing matrix, 3) training and making predictions is very efficient since the number of Gaussian Processes (GP) operations (e.g. inverting the inducing point's covariance matrix) do not depend on the number of processes. This makes the ETGP particularly suited for multi-class problems with a very large number of classes, which are the problems studied in this work. ETGPs exploit the recently proposed Transformed Gaussian Process (TGP), a stochastic process specified by transforming a Gaussian Process using an invertible transformation. However, unlike TGPs, ETGPs are constructed by transforming a single sample from a GP using C invertible transformations. We derive an efficient sparse variational inference algorithm for the proposed model and demonstrate its utility in 5 classification tasks which include low/medium/large datasets and a different number of classes, ranging from just a few to hundreds. Our results show that ETGPs, in general, outperform state-of-the-art methods for multi-class classification based on GPs, and have a lower computational cost (around one order of magnitude smaller).
