Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Latent Gaussian process with composite likelihoods and numerical
  quadrature   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.

24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol