Latent Gaussian process with composite likelihoods and numerical quadrature

Siddharth Ramchandran*, Miika Koskinen, Harri Lahdesmaki

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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Abstract

Clinical patient records are an example of high-dimensional data that is typically collected from disparate sources and comprises of multiple likelihoods with noisy as well as missing values. In this work, we propose an unsupervised generative model that can learn a low-dimensional representation among the observations in a latent space, while making use of all available data in a heterogeneous data setting with missing values. We improve upon the existing Gaussian process latent variable model (GPLVM) by incorporating multiple likelihoods and deep neural network parameterised back-constraints to create a non-linear dimensionality reduction technique for heterogeneous data. In addition, we develop a variational inference method for our model that uses numerical quadrature. We establish the effectiveness of our model and compare against existing GPLVM methods on a standard benchmark dataset as well as on clinical data of Parkinson's disease patients treated at the HUS Helsinki University Hospital.

Original languageEnglish
Title of host publication24TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS (AISTATS)
EditorsA Banerjee, K Fukumizu
PublisherMICROTOME PUBLISHING
Number of pages10
Publication statusPublished - 2021
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Artificial Intelligence and Statistics - Virtual, Online
Duration: 13 Apr 202115 Apr 2021
Conference number: 24

Publication series

NameProceedings of Machine Learning Research
PublisherMICROTOME PUBLISHING
Volume130
ISSN (Print)2640-3498

Conference

ConferenceInternational Conference on Artificial Intelligence and Statistics
Abbreviated titleAISTATS
CityVirtual, Online
Period13/04/202115/04/2021

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