2010年
Learning non-linear dynamical systems by alignment of local linear models
Proceedings - International Conference on Pattern Recognition
- ,
- ,
- 開始ページ
- 1084
- 終了ページ
- 1087
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1109/ICPR.2010.271
- 出版者・発行元
- IEEE Computer Society
Learning dynamical systems is one of the important problems in many fields. In this paper, we present an algorithm for learning non-linear dynamical systems which works by aligning local linear models, based on a probabilistic formulation of subspace identification. Because the procedure for constructing a state sequence in subspace identification can be interpreted as the CCA between past and future observation sequences, we can derive a latent variable representation for this problem. Therefore, as in a similar manner to the recent works on learning a mixture of probabilistic models, we obtain a framework for constructing a state space by aligning local linear coordinates. This leads to a prominent algorithm for learning non-linear dynamical systems. Finally, we apply our method to motion capture data and show how our algorithm works well. © 2010 IEEE.
- リンク情報
- ID情報
-
- DOI : 10.1109/ICPR.2010.271
- ISSN : 1051-4651
- DBLP ID : conf/icpr/JokoKY10
- SCOPUS ID : 78149472826