Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
ICML '06 Proceedings of the 23rd international conference on Machine learning
LDA-based document models for ad-hoc retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Topics over time: a non-Markov continuous-time model of topical trends
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Topic tracking model for analyzing consumer purchase behavior
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Online multiscale dynamic topic models
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Topic tracking language model for speech recognition
Computer Speech and Language
DTTM: a discriminative temporal topic model for facial expression recognition
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Sequential Modeling of Topic Dynamics with Multiple Timescales
ACM Transactions on Knowledge Discovery from Data (TKDD)
Cross-domain collaborative filtering over time
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Fast mining and forecasting of complex time-stamped events
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Personalized time-aware tweets summarization
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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Traditional probabilistic mixture models such as Latent Dirichlet Allocation imply that data records (such as documents) are fully exchangeable. However, data are naturally collected along time, thus obey some order in time. In this paper, we present Dynamic Mixture Models (DMMs) for online pattern discovery in multiple time series. DMMs do not have the noticeable drawback of the SVD-based methods for data streams: negative values in hidden variables are often produced even with all non-negative inputs. We apply DMM models to two real-world datasets, and achieve significantly better results with intuitive interpretation.