Abstract
2014 We present a Bayesian nonparametric framework for multilevel clustering which utilizes group- level context information to simultaneously discover low-dimensional structures of the group contents and partitions groups into clusters. Using the Dirichlet process as the building block, our model constructs a product base-measure with a nested structure to accommodate content and context observations at multiple levels. The proposed model possesses properties that link the nested Dinchiet processes (nDP) and the Dirichlet process mixture models (DPM) in an interesting way: integrating out all contents results in the DPM over contexts, whereas integrating out group-specific contexts results in the nDP mixture over content variables. We provide a Polyaurn view of the model and an efficient collapsed Gibbs inference procedure. Extensive experiments on real-world datasets demonstrate the advantage of utilizing context information via our model in both text and image domains.
Original language | English |
---|---|
Title of host publication | 31st International Conference on Machine Learning - (ICML 2014) |
Subtitle of host publication | Beijing, China 21-26 June 2014 - Volume 1 of 5 |
Editors | Eric P. Xing, Tony Jebara |
Place of Publication | Red Hook NY USA |
Publisher | International Machine Learning Society (IMLS) |
Pages | 481-501 |
Number of pages | 21 |
Volume | 32 |
ISBN (Electronic) | 9781634393973 |
Publication status | Published - 2014 |
Externally published | Yes |
Event | International Conference on Machine Learning 2014 - Beijing International Convention Center (BICC), Beijing, China Duration: 21 Jun 2014 → 26 Jun 2014 Conference number: 31st http://icml.cc/2014/ |
Conference
Conference | International Conference on Machine Learning 2014 |
---|---|
Abbreviated title | ICML 2014 |
Country/Territory | China |
City | Beijing |
Period | 21/06/14 → 26/06/14 |
Internet address |