Statistical Latent Space Approach for Mixed Data Modelling and Applications

TL;DR

This paper introduces an advanced RBM-based model for effectively transforming and analyzing large-scale mixed data, improving performance in medical and image retrieval applications.

Contribution

It extends the mixed-variate restricted Boltzmann machine with parameter sharing, structured sparsity, and distance metric learning for better mixed data modeling.

Findings

Models outperform baseline methods in medical data analysis.

Models outperform state-of-the-art in image retrieval.

Effective handling of large-scale mixed data.

Abstract

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.