The Journal of Machine Learning Research
Variational methods for the Dirichlet process
ICML '04 Proceedings of the twenty-first international conference on Machine learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Music Analysis Using Hidden Markov Mixture Models
IEEE Transactions on Signal Processing
Community evolution detection in dynamic heterogeneous information networks
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Online multiscale dynamic topic models
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Evolutionary hierarchical dirichlet processes for multiple correlated time-varying corpora
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Trajectory Analysis and Semantic Region Modeling Using Nonparametric Hierarchical Bayesian Models
International Journal of Computer Vision
Sequential Modeling of Topic Dynamics with Multiple Timescales
ACM Transactions on Knowledge Discovery from Data (TKDD)
Modeling topical trends over continuous time with priors
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Extraction of topic evolutions from references in scientific articles and its GPU acceleration
Proceedings of the 21st ACM international conference on Information and knowledge management
Transfer learning using a nonparametric sparse topic model
Neurocomputing
Real time event detection in twitter
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Dynamic joint sentiment-topic model
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
| Hi-index | 0.00 |
The dynamic hierarchical Dirichlet process (dHDP) is developed to model the time-evolving statistical properties of sequential data sets. The data collected at any time point are represented via a mixture associated with an appropriate underlying model, in the framework of HDP. The statistical properties of data collected at consecutive time points are linked via a random parameter that controls their probabilistic similarity. The sharing mechanisms of the time-evolving data are derived, and a relatively simple Markov Chain Monte Carlo sampler is developed. Experimental results are presented to demonstrate the model.