Mixed-Variate Restricted Boltzmann Machines

Abstract

Modern datasets are becoming heterogeneous. To this end, we present in this paper Mixed-Variate Restricted Boltzmann Machines for simultaneously modelling variables of multiple types and modalities, including binary and continuous responses, categorical options, multicategorical choices, ordinal assessment and category-ranked preferences. Dependency among variables is modeled using latent binary variables, each of which can be interpreted as a particular hidden aspect of the data. The proposed model, similar to the standard RBMs, allows fast evaluation of the posterior for the latent variables. Hence, it is naturally suitable for many common tasks including, but not limited to, (a) as a pre-processing step to convert complex input data into a more convenient vectorial representation through the latent posteriors, thereby offering a dimensionality reduction capacity, (b) as a classifier supporting binary, multiclass, multilabel, and label-ranking outputs, or a regression tool for continuous outputs and (c) as a data completion tool for multimodal and heterogeneous data. We evaluate the proposed model on a large-scale dataset using the world opinion survey results on three tasks: feature extraction and visualization, data completion and prediction.

Originally published in Proceedings of ACML'11

Cited by in corpus (8)

• Generating Multi-label Discrete Patient Records using Generative Adversarial Networks
• Thurstonian Boltzmann Machines: Learning from Multiple Inequalities
• Outlier Detection on Mixed-Type Data: An Energy-based Approach
• Discrete Restricted Boltzmann Machines
• Multilevel Anomaly Detection for Mixed Data
• Learning deep representation of multityped objects and tasks
• Cumulative Restricted Boltzmann Machines for Ordinal Matrix Data Analysis
• Statistical Latent Space Approach for Mixed Data Modelling and Applications