A Survey on Latent Tree Models and Applications
This is an incremental survey paper for researchers in data analysis, offering a review of existing work without introducing new methods or results.
This paper provides a comprehensive survey of latent tree models, summarizing their key ideas, efficient learning methods, and applications in latent structure discovery, multidimensional clustering, and probabilistic inference, based on theoretical and methodological developments over the past decade.
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.