DeepAI AI Chat
Log In Sign Up

Statistical Latent Space Approach for Mixed Data Modelling and Applications

by   Tu Dinh Nguyen, et al.
Deakin University

The analysis of mixed data has been raising challenges in statistics and machine learning. One of two most prominent challenges is to develop new statistical techniques and methodologies to effectively handle mixed data by making the data less heterogeneous with minimum loss of information. The other challenge is that such methods must be able to apply in large-scale tasks when dealing with huge amount of mixed data. To tackle these challenges, we introduce parameter sharing and balancing extensions to our recent model, the mixed-variate restricted Boltzmann machine (MV.RBM) which can transform heterogeneous data into homogeneous representation. We also integrate structured sparsity and distance metric learning into RBM-based models. Our proposed methods are applied in various applications including latent patient profile modelling in medical data analysis and representation learning for image retrieval. The experimental results demonstrate the models perform better than baseline methods in medical data and outperform state-of-the-art rivals in image dataset.


page 1

page 27

page 28


Effective Learning of Probabilistic Models for Clinical Predictions from Longitudinal Data

With the expeditious advancement of information technologies, health-rel...

Statistical learning methods for neuroimaging data analysis with applications

The aim of this paper is to provide a comprehensive review of statistica...

Mixed Pooling Multi-View Attention Autoencoder for Representation Learning in Healthcare

Distributed representations have been used to support downstream tasks i...

Mixed-Variate Restricted Boltzmann Machines

Modern datasets are becoming heterogeneous. To this end, we present in t...

Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis

The Bayesian approach to feature extraction, known as factor analysis (F...

Metric Based Few-Shot Graph Classification

Many modern deep-learning techniques do not work without enormous datase...