JAIST Repository >
School of Knowledge Science >
Conference Papers >
Conference Papers >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/9577

Title: Analyzing Multimodal Time Series as Dynamical Systems
Authors: Hidaka, Shohei
Yu, Chen
Keywords: Multi-stream time series
multi-agent communication
symbol dynamics
generating partition
Issue Date: 2010
Publisher: Association for Computing Machinery
Magazine name: Proceedings of 12th International Conference on Multimodal Interfaces and 7th Workshop on Machine Learning for Multimodal Interaction
Start page: Article No.53
DOI: 10.1145/1891903.1891968
Abstract: 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
Material Type: author
Appears in Collections:a11-1. 会議発表論文 (Conference Papers)

Files in This Item:

File Description SizeFormat
15838-1.pdf1899KbAdobe PDFView/Open

All items in DSpace are protected by copyright, with all rights reserved.


Contact : Library Information Section, Japan Advanced Institute of Science and Technology