Gaussian process classification using posterior linearisation

Abstract--This paper proposes a new algorithm for Gaussian process classification based on posterior linearisation (PL). In PL, a Gaussian approximation to the posterior density is obtained iteratively using the best possible linearisation of the conditional mean of the labels and accounting for the linearisation error . Considering three widely-used likelihood functions, in general, PL provides lower classification errors in real data sets than expectation propagation and Laplace algorithms. Classification is an important problem with a high number of applications, for example, in handwriting and speech recognition, and medical diagnosis [1]. In (supervised) classification, a set of training data points with their corresponding classes are available to learn the underlying structure of the problem. Based on this information, the objective is to infer the classes of new data points. This classification problem can be posed using Gaussian processes (GPs) [2]-[8].