Latent Gaussian process with composite likelihoods and numerical quadrature

Siddharth Ramchandran, Miika Koskinen, Harri Lähdesmäki
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:3718-3726, 2021.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v130-ramchandran21a, title = { Latent Gaussian process with composite likelihoods and numerical quadrature }, author = {Ramchandran, Siddharth and Koskinen, Miika and L{\"a}hdesm{\"a}ki, Harri}, booktitle = {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics}, pages = {3718--3726}, year = {2021}, editor = {Banerjee, Arindam and Fukumizu, Kenji}, volume = {130}, series = {Proceedings of Machine Learning Research}, month = {13--15 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v130/ramchandran21a/ramchandran21a.pdf}, url = {https://proceedings.mlr.press/v130/ramchandran21a.html}, 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. } }
Endnote
%0 Conference Paper %T Latent Gaussian process with composite likelihoods and numerical quadrature %A Siddharth Ramchandran %A Miika Koskinen %A Harri Lähdesmäki %B Proceedings of The 24th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2021 %E Arindam Banerjee %E Kenji Fukumizu %F pmlr-v130-ramchandran21a %I PMLR %P 3718--3726 %U https://proceedings.mlr.press/v130/ramchandran21a.html %V 130 %X 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.
APA
Ramchandran, S., Koskinen, M. & Lähdesmäki, H.. (2021). Latent Gaussian process with composite likelihoods and numerical quadrature . Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 130:3718-3726 Available from https://proceedings.mlr.press/v130/ramchandran21a.html.

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