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.
Original language | English |
---|---|
Title of host publication | Proceedings of the 9th Asian Conference on Machine Learning |
Editors | Min-Ling Zhang, Yung-Kyun Noh |
Pages | 455-470 |
Number of pages | 16 |
Publication status | Published - Nov 2017 |
MoE publication type | A4 Article in a conference publication |
Event | Asian Conference on Machine Learning - Yonsei University, Seoul, Korea, Seoul, Korea, Republic of Duration: 15 Nov 2017 → 17 Nov 2017 Conference number: 9 http://www.acml-conf.org/2017/ |
Publication series
Name | Proceedings of Machine Learning Research |
---|---|
Publisher | PMLR |
Volume | 77 |
ISSN (Electronic) | 1938-7228 |
Conference
Conference | Asian Conference on Machine Learning |
---|---|
Abbreviated title | ACML |
Country | Korea, Republic of |
City | Seoul |
Period | 15/11/2017 → 17/11/2017 |
Internet address |