Twin Gaussian Processes for Binary Classification

Gaussian process classifiers (GPCs) have recently attracted more and more attention from the machine learning community. However, because the posterior needs to be approximated by using a tractable Gaussian distribution, they usually suffer from high computational cost which is prohibitive for practical applications. In this paper, we present a new Gaussian process model termed as twin Gaussian processes for binary classification. The basic idea is to make predictions based on two latent functions with Gaussian process prior, each of which is close to one of the two classes and is as far as possible from the other. Being compared with the published GPCs, the proposed algorithm allows for an explicit inference based on analytical methods, thereby avoiding the high computational cost caused by approximating the posterior with Gaussian distribution. Experimental results on several benchmark data sets show that the proposed algorithm is valid and can achieve superior performance to the published algorithms.