  Multimodal relational data analysis has become of increasing importance in
recent years, for exploring across different domains of data, such as images
and their text tags obtained from social networking services (e.g., Flickr). A
variety of data analysis methods have been developed for visualization; to give
an example, t-Stochastic Neighbor Embedding (t-SNE) computes low-dimensional
feature vectors so that their similarities keep those of the observed data
vectors. However, t-SNE is designed only for a single domain of data but not
for multimodal data; this paper aims at visualizing multimodal relational data
consisting of data vectors in multiple domains with relations across these
vectors. By extending t-SNE, we herein propose Multimodal Relational Stochastic
Neighbor Embedding (MR-SNE), that (1) first computes augmented relations, where
we observe the relations across domains and compute those within each of
domains via the observed data vectors, and (2) jointly embeds the augmented
relations to a low-dimensional space. Through visualization of Flickr and
Animal with Attributes 2 datasets, proposed MR-SNE is compared with other graph
embedding-based approaches; MR-SNE demonstrates the promising performance.
