22,930 research outputs found
A Variational Autoencoder for Heterogeneous Temporal and Longitudinal Data
The variational autoencoder (VAE) is a popular deep latent variable model
used to analyse high-dimensional datasets by learning a low-dimensional latent
representation of the data. It simultaneously learns a generative model and an
inference network to perform approximate posterior inference. Recently proposed
extensions to VAEs that can handle temporal and longitudinal data have
applications in healthcare, behavioural modelling, and predictive maintenance.
However, these extensions do not account for heterogeneous data (i.e., data
comprising of continuous and discrete attributes), which is common in many
real-life applications. In this work, we propose the heterogeneous longitudinal
VAE (HL-VAE) that extends the existing temporal and longitudinal VAEs to
heterogeneous data. HL-VAE provides efficient inference for high-dimensional
datasets and includes likelihood models for continuous, count, categorical, and
ordinal data while accounting for missing observations. We demonstrate our
model's efficacy through simulated as well as clinical datasets, and show that
our proposed model achieves competitive performance in missing value imputation
and predictive accuracy.Comment: Preprin
Multi-Channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease
The joint analysis of biomedical data in Alzheimer's Disease (AD) is
important for better clinical diagnosis and to understand the relationship
between biomarkers. However, jointly accounting for heterogeneous measures
poses important challenges related to the modeling of the variability and the
interpretability of the results. These issues are here addressed by proposing a
novel multi-channel stochastic generative model. We assume that a latent
variable generates the data observed through different channels (e.g., clinical
scores, imaging, ...) and describe an efficient way to estimate jointly the
distribution of both latent variable and data generative process. Experiments
on synthetic data show that the multi-channel formulation allows superior data
reconstruction as opposed to the single channel one. Moreover, the derived
lower bound of the model evidence represents a promising model selection
criterion. Experiments on AD data show that the model parameters can be used
for unsupervised patient stratification and for the joint interpretation of the
heterogeneous observations. Because of its general and flexible formulation, we
believe that the proposed method can find important applications as a general
data fusion technique.Comment: accepted for presentation at MLCN 2018 workshop, in Conjunction with
MICCAI 2018, September 20, Granada, Spai
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