Incremental probabilistic latent semantic analysis for automatic question recommendation
Proceedings of the 2008 ACM conference on Recommender systems
An adaptive threshold framework for event detection using HMM-based life profiles
ACM Transactions on Information Systems (TOIS)
Online New Event Detection Based on IPLSA
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
IPHITS: An Incremental Latent Topic Model for Link Structure
AIRS '09 Proceedings of the 5th Asia Information Retrieval Symposium on Information Retrieval Technology
Incremental Learning of Triadic PLSA for Collaborative Filtering
AMT '09 Proceedings of the 5th International Conference on Active Media Technology
Online learning for PLSA-based visual recognition
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
RPLSA: A novel updating scheme for Probabilistic Latent Semantic Analysis
Computer Speech and Language
Proceedings of the fifth ACM international conference on Web search and data mining
Indices of novelty for emerging topic detection
Information Processing and Management: an International Journal
Incremental visual objects clustering with the growing vocabulary tree
Multimedia Tools and Applications
Term Weighting Schemes for Emerging Event Detection
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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The goal of on-line event analysis is to detect events and their associated documents in real-time from a continuous stream of documents generated by multiple information sources. Existing approaches (e.g., window-based, decay function, and adaptive threshold methods) incorporate the temporal relations of documents into traditional text categorization methods for event analysis. However, these methods suffer from the threshold dependence problem, i.e., their performance is only acceptable for a narrow range of thresholds; thus, it is difficult to designate an appropriate threshold in advance. In this paper, we propose a threshold resilient algorithm, called Incremental Probabilistic Latent Semantic Indexing (IPLSI), which can capture the storyline development of an event without the threshold dependence problem. The IPLSI algorithm is theoretically sound and more efficient than naïve PLSI approaches. The results of the performance evaluation based on the TDT 4 corpus show that the proposed algorithm reduces the error tradeoff cost of event detection by as much as 14.51% and increases the threshold range for acceptable performance by 300% - 800%