Learning and Inferring Motion Patterns using Parametric Segmental Switching Linear Dynamic Systems
International Journal of Computer Vision
Data-driven MCMC for learning and inference in switching linear dynamic systems
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
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We propose a novel visualization algorithm for high-dimensional time-series data. In contrast to most visualization techniques, we do not assume consecutive data points to be independent. The basic model is a linear dynamical system which can be seen as a dynamic extension of a probabilistic principal component model. A further extension to a particular switching linear dynamical system allows a representation of complex data onto multiple and even a hierarchy of plots. Using sensible approximations based on expectation propagation, the projections can be performed in essentially the same order of complexity as their static counterpart. We apply our method on a real-world data set with sensor readings from a paper machine.