2006
NIPS
NeurIPS 2006
A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems
Abstract
In this paper, we present a subspace method for learning nonlinear dynamical systems based on stochastic realization, in which state vectors are chosen using kernel canonical correlation analysis, and then state-space systems are identified through regression with the state vectors. We construct the theoretical underpinning and derive a concrete algorithm for nonlinear identification. The obtained algorithm needs no iterative optimization procedure and can be implemented on the basis of fast and reliable numerical schemes. The simulation result shows that our algorithm can express dynamics with a high degree of accuracy.
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Conference Pioneer
— NIPS 2006
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Interdisciplinary Bridge
— Data Science & Analytics and Machine Learning
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Trend Setter
— Theory
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Keyword Pioneer
— subspace learning
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
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Hot Topic Early Bird
— state-space model
Authors
Topics
Machine Learning > Core Methods > Representation Learning
Machine Learning > Learning Types > Unsupervised Learning
Machine Learning > Optimization & Theory > Theory
Data Science & Analytics > Methods > Time Series
Data Science & Analytics > Methods > Time Series Analysis
Mathematics & Optimization > Mathematics > Stochastic Processes
Machine Learning > Learning Types > Representation Learning
Machine Learning > Core Methods > Kernel Methods
Mathematics & Optimization > Optimization > Kernel Methods