NDLI: Probabilistic latent semantic visualization: topic model for visualizing documents

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Probabilistic latent semantic visualization: topic model for visualizing documents

Content Provider	ACM Digital Library
Author	Yamada, Takeshi Ueda, Naonori Iwata, Tomoharu
Abstract	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.
Starting Page	363
Ending Page	371
Page Count	9
File Format	PDF
ISBN	9781605581934
DOI	10.1145/1401890.1401937
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-08-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Visualization Probabilistic latent semantic analysis Topic model
Content Type	Text
Resource Type	Article

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