Publication Type

Journal Article

Publication Date

4-2016

Abstract

Visualization of high-dimensional data, such as text documents, is useful to map out the similarities among various data points. In the high-dimensional space, documents are commonly represented as bags of words, with dimensionality equal to the vocabulary size. Classical approaches to document visualization directly reduce this into visualizable two or three dimensions. Recent approaches consider an intermediate representation in topic space, between word space and visualization space, which preserves the semantics by topic modeling. While aiming for a good fit between the model parameters and the observed data, previous approaches have not considered the local consistency among data instances. We consider the problem of semantic visualization by jointly modeling topics and visualization on the intrinsic document manifold, modeled using a neighborhood graph. Each document has both a topic distribution and visualization coordinate. Specifically, we propose an unsupervised probabilistic model, called SEMAFORE, which aims to preserve the manifold in the lower-dimensional spaces through a neighborhood regularization framework designed for the semantic visualization task. To validate the efficacy of SEMAFORE, our comprehensive experiments on a number of real-life text datasets of news articles and Web pages show that the proposed methods outperform the state-of-the-art baselines on objective evaluation metrics.

Discipline

Computer Sciences | Graphics and Human Computer Interfaces

Research Areas

Data Management and Analytics

Publication

Journal of Artificial Intelligence Research

Volume

55

First Page

1091

Last Page

1133

ISSN

1076-9757

Identifier

doi:10.1613/jair.4983

Publisher

Association for the Advancement of Artificial Intelligence / AI Access Foundation

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://jair.org/papers/paper4983.html

Comments

JAIR Award Winning Papers Track: AAAI 2014 Honorable Mention for Outstanding Paper

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