Conference Proceeding Article
Classical approaches to visualization directly reduce a document's high-dimensional representation into visualizable two or three dimensions, using techniques such as multidimensional scaling. More recent approaches consider an intermediate representation in topic space, between word space and visualization space, which preserves the semantics by topic modeling. We call the latter semantic visualization problem, as it seeks to jointly model topic and visualization. While previous approaches aim to preserve the global consistency, they do not consider the local consistency in terms of the intrinsic geometric structure of the document manifold. We therefore propose an unsupervised probabilistic model, called Semafore, which aims to preserve the manifold in the lower-dimensional spaces. Comprehensive experiments on several real-life text datasets of news articles and web pages show that Semafore significantly outperforms the state-of-the-art baselines on objective evaluation metrics.
visualization, topic modeling, manifold, regularization
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
Proceedings of the 28th AAAI Conference on Artificial Intelligence: July 27-31, 2014, Québec City
City or Country
Menlo Park, CA
LE, Tuan Minh Van and LAUW, Hady W..
Manifold Learning for Jointly Modeling Topic and Visualization. (2014). Proceedings of the 28th AAAI Conference on Artificial Intelligence: July 27-31, 2014, Québec City. 1960-1967. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2248
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
This is an implementation of SEMAFORE - a semantic visualization method from Le & Lauw (AAAI 2014, JAIR 2016).