第25回統計的機械学習セミナー(2015年8月18日)
Time: August 18 (Tue), 2015. 15:00-
Place：Seminar Room 5
Speaker:
Gregor Gebhardt (TU Darmstadt, Germany)
Title:
The Generalized Kernel Kalman Filter - Learning Forward Models from High
Dimensional Observations
Abstract:
Learning forward models from high-dimensional
partial observations of the real state is a challenging machine learning problem. Recently, non-parametric inference methods have been pro-
posed to tackle such problems. However, such methods either do not provide an uncertainty estimate, are computationally expensive, or can only
be applied to a limited set of problems. We generalize the formulation of Kalman Filters (KF)
embeddings into a reproducing kernel Hilbert
space (RKHS) to be applicable to systems with
high-dimensional, partial observations. Our formulation provides probabilistic state estimations
and predictions for non-linear dynamical systems
that can also be directly learned from the observations. Additionally, we propose an alternative
formulation of the RKHS embedding of a conditional density that allows to learn from large
data sets, while maintaining computational efficiency. We show on a nonlinear state estimation
task with high dimensional observations that our approach provides an improved estimation accuracy.