Scalable Gaussian Process Regression Using Deep Neural Networks / 3576
Wenbing Huang, Deli Zhao, Fuchun Sun, Huaping Liu, Edward Chang
We propose a scalable Gaussian process model for regression by applying a deep neural network as the feature-mapping function. We first pretrain the deep neural network with a stacked denoising auto-encoder in an unsupervised way. Then, we perform a Bayesian linear regression on the top layer of the pre-trained deep network. The resulting model, Deep-Neural-Network-based Gaussian Process (DNN-GP), can learn much more meaningful representation of the data by the finite-dimensional but deep-layered feature-mapping function. Unlike standard Gaussian processes, our model scales well with the size of the training set due to the avoidance of kernel matrix inversion. Moreover, we present a mixture of DNN-GPs to further improve the regression performance. For the experiments on three representative large datasets, our proposed models significantly outperform the state-of-the-art algorithms of Gaussian process regression.