Heterogeneous co-occurrence embedding for visual information exploration

TL;DR

This paper introduces a novel embedding technique for visualizing asymmetric relationships in heterogeneous co-occurrence data across multiple domains, enhancing interpretability and exploration of complex dependencies.

Contribution

It presents a new embedding method that maps heterogeneous elements into two-dimensional spaces to preserve mutual information and visualize asymmetric relationships, extending to multiple domains.

Findings

Effective visualization of asymmetric inter-domain relationships

Application to diverse datasets demonstrating interpretability

Extension to multi-domain co-occurrence analysis

Abstract

This paper proposes an embedding method for co-occurrence data aimed at visual information exploration. We consider cases where co-occurrence probabilities are measured between pairs of elements from heterogeneous domains. The proposed method maps these heterogeneous elements into corresponding two-dimensional latent spaces, enabling visualization of asymmetric relationships between the domains. The key idea is to embed the elements in a way that maximizes their mutual information, thereby preserving the original dependency structure as much as possible. This approach can be naturally extended to cases involving three or more domains, using a generalization of mutual information known as total correlation. For inter-domain analysis, we also propose a visualization method that assigns colors to the latent spaces based on conditional probabilities, allowing users to explore asymmetric relationships interactively. We demonstrate the utility of the method through applications to an adjective-noun dataset, the NeurIPS dataset, and a subject-verb-object dataset, showcasing both intra- and inter-domain analysis.