Statistical Latent Space Approach for Mixed Data Modelling and Applications

08/18/2017
by   Tu Dinh Nguyen, et al.
0

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.

READ FULL TEXT

page 1

page 27

page 28

research
11/02/2018

Effective Learning of Probabilistic Models for Clinical Predictions from Longitudinal Data

With the expeditious advancement of information technologies, health-rel...
research
10/17/2022

Statistical learning methods for neuroimaging data analysis with applications

The aim of this paper is to provide a comprehensive review of statistica...
research
10/14/2019

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

Distributed representations have been used to support downstream tasks i...
research
04/30/2023

MD-Manifold: A Medical-Distance-Based Representation Learning Approach for Medical Concept and Patient Representation

Effectively representing medical concepts and patients is important for ...
research
08/06/2014

Mixed-Variate Restricted Boltzmann Machines

Modern datasets are becoming heterogeneous. To this end, we present in t...
research
01/24/2020

Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis

The Bayesian approach to feature extraction, known as factor analysis (F...
research
06/08/2022

Metric Based Few-Shot Graph Classification

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

Please sign up or login with your details

Forgot password? Click here to reset