Poster
Gaussian Copula Embeddings
Chien Lu · Jaakko Peltonen
Hall J (level 1) #342
Keywords: [ heterogeneous data ] [ Representation Learning ] [ gaussian copula ] [ vectorial embedding ] [ probabilistic models ]
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.