Name
Abstract | ||
|---|---|---|
In data analysis, latent variables play a central role because they help provide powerful insights into a wide variety of phenomena, ranging from biological to human sciences. The latent tree model, a particular type of probabilistic graphical models, deserves attention. Its simple structure - a tree - allows simple and efficient inference, while its latent variables capture complex relationships. In the past decade, the latent tree model has been subject to significant theoretical and methodological developments. In this review, we propose a comprehensive study of this model. First we summarize key ideas underlying the model. Second we explain how it can be efficiently learned from data. Third we illustrate its use within three types of applications: latent structure discovery, multidimensional clustering, and probabilistic inference. Finally, we conclude and give promising directions for future researches in this field. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1613/jair.3879 | Journal of Artificial Intelligence Research |
Keywords | DocType | Volume |
complex relationship,efficient inference,data analysis,central role,probabilistic graphical model,latent variable,probabilistic inference,latent structure discovery,simple structure,latent tree model | Journal | abs/1402.0577 |
Issue | ISSN | Citations |
1 | Journal Of Artificial Intelligence Research, Volume 47, pages
157-203, 2013 | 25 |
PageRank | References | Authors |
1.21 | 43 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raphaël Mourad | 1 | 79 | 5.70 |
| Christine Sinoquet | 2 | 98 | 10.86 |
| Nevin .L Zhang | 3 | 895 | 97.21 |
| Tengfei Liu | 4 | 92 | 7.09 |
| Tengfei Liu | 5 | 488 | 34.13 |