A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings

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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.
Original languageEnglish
Title of host publicationProceedings of the 9th Asian Conference on Machine Learning
EditorsMin-Ling Zhang, Yung-Kyun Noh
Number of pages16
Publication statusPublished - 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

Publication series

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


ConferenceAsian Conference on Machine Learning
Abbreviated titleACML
CountryKorea, Republic of
Internet address

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