Discriminative training for Convolved Multiple-Output Gaussian processes

TL;DR

This paper explores discriminative training of multi-output Gaussian processes for pattern recognition tasks, demonstrating improved performance over generative methods and hidden Markov models in emotion, activity, and face recognition.

Contribution

It introduces a discriminative training approach for multi-output Gaussian processes and compares its effectiveness against traditional generative training and hidden Markov models.

Findings

Discriminative training improves recognition accuracy.

Multi-output Gaussian processes outperform hidden Markov models.

Method is effective across multiple pattern recognition tasks.

Abstract

Multi-output Gaussian processes (MOGP) are probability distributions over vector-valued functions, and have been previously used for multi-output regression and for multi-class classification. A less explored facet of the multi-output Gaussian process is that it can be used as a generative model for vector-valued random fields in the context of pattern recognition. As a generative model, the multi-output GP is able to handle vector-valued functions with continuous inputs, as opposed, for example, to hidden Markov models. It also offers the ability to model multivariate random functions with high dimensional inputs. In this report, we use a discriminative training criteria known as Minimum Classification Error to fit the parameters of a multi-output Gaussian process. We compare the performance of generative training and discriminative training of MOGP in emotion recognition, activity recognition, and face recognition. We also compare the proposed methodology against hidden Markov models trained in a generative and in a discriminative way.