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このアイテムの引用には次の識別子を使用してください: http://hdl.handle.net/10119/9577

タイトル: Analyzing Multimodal Time Series as Dynamical Systems
著者: Hidaka, Shohei
Yu, Chen
キーワード: Multi-stream time series
multi-agent communication
symbol dynamics
generating partition
発行日: 2010
出版者: Association for Computing Machinery
誌名: Proceedings of 12th International Conference on Multimodal Interfaces and 7th Workshop on Machine Learning for Multimodal Interaction
開始ページ: Article No.53
DOI: 10.1145/1891903.1891968
抄録: 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.
Rights: (c) ACM, 2010. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of 12th International Conference on Multimodal Interfaces and 7th Workshop on Machine Learning for Multimodal Interaction, 2010, Article No.53. http://doi.acm.org/10.1145/1891903.1891968
URI: http://hdl.handle.net/10119/9577
資料タイプ: author
出現コレクション:a11-1. 会議発表論文 (Conference Papers)

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