| Author(s): |
Faleiros, Thiago de Paulo
;
Lopes, Alneu de Andrade
;
Yang, Q
;
Wooldridge, M
Total Authors: 4
|
| Document type: | Journal article |
| Source: | PROCEEDINGS OF THE TWENTY-FOURTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI); v. N/A, p. 2-pg., 2015-01-01. |
| Abstract | |
This article presents a bipartite graph propagation method to be applied to different tasks in the machine learning unsupervised domain, such as topic extraction and clustering. We introduce the objectives and hypothesis that motivate the use of graph based method, and we give the intuition of the proposed Bipartite Graph Propagation Algorithm. The contribution of this study is the development of new method that allows the use of heuristic knowledge to discover topics in textual data easier than it is possible in the traditional mathematical formalism based on Latent Dirichlet Allocation (LDA). Initial experiments demonstrate that our Bipartite Graph Propagation algorithm return good results in a static context (offline algorithm). Now, our research is focusing on big amount of data and dynamic context (online algorithm). (AU) | |
| FAPESP's process: | 11/23689-9 - Propagation in bipartite graphs for Topic Extraction in Data Streams |
| Grantee: | Thiago de Paulo Faleiros |
| Support Opportunities: | Scholarships in Brazil - Doctorate |