Publication Type
Journal Article
Version
publishedVersion
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
Databases and Information Systems | Numerical Analysis and Scientific Computing
Research Areas
Data Science and Engineering
Publication
Journal of Artificial Intelligence Research
Volume
55
First Page
1091
Last Page
1133
ISSN
1076-9757
Identifier
10.1613/jair.4983
Publisher
Association for the Advancement of Artificial Intelligence / AI Access Foundation
Citation
LE, Tuan Minh Van and LAUW, Hady W..
Semantic Visualization with Neighborhood Graph Regularization. (2016). Journal of Artificial Intelligence Research. 55, 1091-1133.
Available at: https://ink.library.smu.edu.sg/sis_research/3252
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1613/jair.4983
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons
Comments
JAIR Award Winning Papers Track: AAAI 2014 Honorable Mention for Outstanding Paper