Scalable Hierarchical Mixture of Gaussian Processes for Pattern Classification

Authors

Thi Nhat Anh Nguyen, University of WollongongFollow
Abdesselam Bouzerdoum, University of WollongongFollow
Son Lam Phung, University of WollongongFollow

RIS ID

130860

Publication Details

T. Nguyen, A. Bouzerdoum & S. Phung, "Scalable Hierarchical Mixture of Gaussian Processes for Pattern Classification," in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): Proceedings, 2018, pp. 2466-2470.

Abstract

This paper introduces a novel Gaussian process (GP) classification method that combines advantages of global and local GP approximators through a two-layer hierarchical model. The upper layer consists of a global sparse GP to coarsely model the entire dataset. The lower layer is a mixture of GP experts which uses local information to learn a fine-grained model. A variational inference algorithm is developed for simultaneous learning of the global GP, the experts and the gating network. Stochastic optimization can be employed for large-scale problems. Experiments on benchmark binary classification datasets demonstrate the advantages of the method in terms of scalability and classification accuracy.