Gaussian Copula Embeddings

Chien Lu, Jaakko Peltonen

Keywords: representation learning, gaussian copula, heterogeneous data, vectorial embedding, probabilistic models

TL;DR: We introduce a vectorial embedding model for heterogeneous observation data using Gaussian copula.

Abstract: Learning latent vector representations via embedding models has been shown promising in machine learning. However, most of the embedding models are still limited to a single type of observation data. We propose a Gaussian copula embedding model to learn latent vector representations of items in a heterogeneous data setting. The proposed model can effectively incorporate different types of observed data and, at the same time, yield robust embeddings. We demonstrate the proposed model can effectively learn in many different scenarios, outperforming competing models in modeling quality and task performance.