Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Visual Query Language for Relational Knowledge Discovery TITLE2:
A Visual Query Language for Relational Knowledge Discovery TITLE2:
Statistical models and analysis techniques for learning in relational data
Statistical models and analysis techniques for learning in relational data
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
A New Distance Measure for Model-Based Sequence Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Social Computing, Behavioral Modeling, and Prediction
Social Computing, Behavioral Modeling, and Prediction
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
Coupled Behavior Analysis with Applications
IEEE Transactions on Knowledge and Data Engineering
Maximum margin clustering on evolutionary data
Proceedings of the 21st ACM international conference on Information and knowledge management
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In stock markets, an emerging challenge for surveillance is that a group of hidden manipulators collaborate with each other to manipulate the price movement of securities. Recently, the coupled hidden Markov model (CHMM)-based coupled behavior analysis (CBA) has been proposed to consider the coupling relationships in the above group-based behaviors for manipulation detection. From the modeling perspective, however, this requires overall aggregation of the behavioral data to cater for the CHMM modeling, which does not differentiate the coupling relationships presented in different forms within the aggregated behaviors and degrade the capability for further anomaly detection. Thus, this paper suggests a general CBA framework for detecting group-based market manipulation by capturing more comprehensive couplings and proposes two variant implementations, which are hybrid coupling (HC)-based and hierarchical grouping (HG)-based respectively. The proposed framework consists of three stages. The first stage, qualitative analysis, generates possible qualitative coupling relationships between behaviors with or without domain knowledge. In the second stage, quantitative representation of coupled behaviors is learned via proper methods. For the third stage, anomaly detection algorithms are proposed to cater for different application scenarios. Experimental results on data from a major Asian stock market show that the proposed framework outperforms the CHMM-based analysis in terms of detecting abnormal collaborative market manipulations. Additionally, the two different implementations are compared with their effectiveness for different application scenarios.