document: Probabilistic latent semantic visualization : topic model for visualizing documents 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 two - or three-dimensional Euclidean space , or visualization space . The topic proportions of a document 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 .
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