Discriminative training for Convolved Multiple-Output Gaussian processes
This work addresses pattern recognition challenges in fields like emotion and activity analysis by introducing a discriminative training method for MOGP, though it is incremental as it adapts existing criteria to a known model.
The authors tackled the problem of improving multi-output Gaussian processes (MOGP) for pattern recognition by applying discriminative training using Minimum Classification Error, and found that this approach outperformed generative training and hidden Markov models in emotion, activity, and face recognition tasks.
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.