Gómez-González, S., Álvarez, M.A. orcid.org/0000-0002-8980-4472, García, H.F. et al. (2 more authors) (2015) Discriminative training for Convolved Multiple-Output Gaussian processes. In: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. 20th Iberoamerican Congress, CIARP 2015, November 9-12, 2015, Montevideo, Uruguay. Lecture Notes in Computer Science book series (9423). , pp. 595-602. ISBN 978-3-319-25750-1
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015 Springer Verlag. This is an author produced version of a paper subsequently published in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 15 Aug 2017 15:39 |
Last Modified: | 28 Mar 2018 20:18 |
Published Version: | https://doi.org/10.1007/978-3-319-25751-8_71 |
Status: | Published |
Series Name: | Lecture Notes in Computer Science book series |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-319-25751-8_71 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:120151 |