2008 ProbabilisticLatentSemanticVisu

• (Iwata et al., 2008) ⇒ Tomoharu Iwata, Takeshi Yamada, and Naonori Ueda. (2008). “Probabilistic Latent Semantic Visualization: Topic Model for Visualizing Documents.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401937

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Cited By

• http://scholar.google.com/scholar?q=%22Probabilistic+latent+semantic+visualization%3A+topic+model+for+visualizing+documents%22+2008
• http://portal.acm.org/citation.cfm?doid=1401890.1401937&preflayout=flat#citedby

Quotes

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 ProbabilisticLatentSemanticVisu	Naonori Ueda Tomoharu Iwata Takeshi Yamada			Probabilistic Latent Semantic Visualization: Topic Model for Visualizing Documents		KDD-2008 Proceedings		10.1145/1401890.1401937		2008