2014 SemanticVisualizationforSpheric

• (Le & Lauw, 2014) ⇒ Tuan M.V. Le, and Hady W. Lauw. (2014). “Semantic Visualization for Spherical Representation.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623620

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Semantic+Visualization+for+Spherical+Representation
• http://dl.acm.org/citation.cfm?id=2623330.2623620&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; dimensionality reduction; generative model; l2-normalized vector; miscellaneous; semantic visualization; spherical semantic embedding; spherical space; topic model

Abstract

Visualization of high-dimensional data such as text documents is widely applicable. The traditional means is to find an appropriate embedding of the high-dimensional representation in a low-dimensional visualizable space. As topic modeling is a useful form of dimensionality reduction that preserves the semantics in documents, recent approaches aim for a visualization that is consistent with both the original word space, as well as the semantic topic space. In this paper, we address the semantic visualization problem. Given a corpus of documents, the objective is to simultaneously learn the topic distributions as well as the visualization coordinates of documents. We propose to develop a semantic visualization model that approximates L2-normalized data directly. The key is to associate each document with three representations: a coordinate in the visualization space, a multinomial distribution in the topic space, and a directional vector in a high-dimensional unit hypersphere in the word space. We join these representations in a unified generative model, and describe its parameter estimation through variational inference. Comprehensive experiments on real-life text datasets show that the proposed method outperforms the existing baselines on objective evaluation metrics for visualization quality and topic interpretability.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 SemanticVisualizationforSpheric	Tuan M.V. Le Hady W. Lauw			Semantic Visualization for Spherical Representation				10.1145/2623330.2623620		2014