A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Details

Original languageEnglish
Title of host publicationProceedings of the 9th Asian Conference on Machine Learning
EditorsMin-Ling Zhang, Yung-Kyun Noh
Pages455-470
Number of pages16
StatePublished - Nov 2017
MoE publication typeA4 Article in a conference publication
EventAsian Conference on Machine Learning - Yonsei University, Seoul, Korea, Seoul, Korea, Republic of
Duration: 15 Nov 201717 Nov 2017
Conference number: 9
http://www.acml-conf.org/2017/

Publication series

NameProceedings of Machine Learning Research
PublisherPMLR
Volume77
ISSN (Electronic)1938-7228

Conference

ConferenceAsian Conference on Machine Learning
Abbreviated titleACML
CountryKorea, Republic of
CitySeoul
Period15/11/201717/11/2017
Internet address

Researchers

Research units

Abstract

We introduce a novel kernel that models input-dependent couplings across multiple latent processes. The pairwise joint kernel measures covariance along inputs and across different latent signals in a mutually-dependent fashion. A latent correlation Gaussian process (LCGP) model combines these non-stationary latent components into multiple outputs by an input-dependent mixing matrix. Probit classification and support for multiple observation sets are derived by Variational Bayesian inference. Results on several datasets indicate that the LCGP model can recover the correlations between latent signals while simultaneously achieving state-of-the-art performance. We highlight the latent covariances with an EEG classification dataset where latent brain processes and their couplings simultaneously emerge from the model.

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