We propose a visualization method based on a topic model for discrete data such as documents. Unlike conventional visualization methods based on pairwise distances such as multi-dimensional scaling, we consider a mapping from the visualization space into the space of documents as a generative process of documents. In the model, both documents and topics are assumed to have latent coordinates in a twoor three-dimensional Euclidean space, or visualization space. The topic proportions of a

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... are determined by the distances between the document and the topics in the visualization space, and each word is drawn from one of the topics according to its topic proportions. A visualization, i.e. latent coordinates of documents, can be obtained by fitting the model to a given set of documents using the EM algorithm, resulting in documents with similar topics being embedded close together. We demonstrate the effectiveness of the proposed model by visualizing document and movie data sets, and quantitatively compare it with conventional visualization methods.