Bayesian Embedding of Co-occurrence Data for Query-Based Visualization

We propose a generative probabilistic model for visualizing co-occurrence data. In co-occurrence data, there are a number of entities and the data includes the frequency of two entities co-occurring. We propose a Bayesian approach to infer the latent variables. Given the intractability of inference for the posterior distribution, we use approximate inference via variational approaches. The proposed Bayesian approach enables accurate embedding in high-dimensional space which is not useful for visualization. Therefore, we propose a method to embed a filtered number of entities for a query -- query-based visualization. Our experiments show that our proposed models outperform co-occurrence data embedding, the state-of-the-art model for visualizing co-occurrence data.