Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Markov random field models in computer vision
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
Data mining approaches for intrusion detection
SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
Investigating multimodal real-time patterns of joint attention in an hri word learning task
Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction
Active Information Selection: Visual Attention Through the Hands
IEEE Transactions on Autonomous Mental Development
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We propose a novel approach to discovering latent structures from multimodal time series. We view a time series as observed data from an underlying dynamical system. In this way, analyzing multimodal time series can be viewed as finding latent structures from dynamical systems. In light this, our approach is based on the concept of generating partition which is the theoretically best symbolization of time series maximizing the information of the underlying original continuous dynamical system. However, generating partition is difficult to achieve for time series without explicit dynamical equations. Different from most previous approaches that attempt to approximate generating partition through various deterministic symbolization processes, our algorithm maintains and estimates a probabilistic distribution over a symbol set for each data point in a time series. To do so, we develop a Bayesian framework for probabilistic symbolization and demonstrate that the approach can be successfully applied to both simulated data and empirical data from multimodal agent-agent interactions. We suggest this unsupervised learning algorithm has a potential to be used in various multimodal datasets as first steps to identify underlying structures between temporal variables.