Monte Carlo Filtering Using Kernel Embedding of Distributions
By Motonobu Kanagawa, Yu Nishiyama, Arthur Gretton, Kenji Fukumizu. 2014
14 citations recorded by the source corpus.
Recent advances of kernel methods have yielded a framework for representing probabilities using a reproducing kernel Hilbert space, called kernel embedding of distributions. In this paper, we propose a Monte Carlo filtering algorithm based on kernel embeddings. The proposed method is applied to state-space models where sampling from the transition model is possible, while the observation model is to be learned from training samples without assuming a parametric model. As a theoretical basis of the proposed method, we prove consistency of the Monte Carlo method combined with kernel embeddings. Experimental results on synthetic models and real vision-based robot localization confirm the effectiveness of the proposed approach.
Subfields: Aerospace Engineering, Artificial Intelligence
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In Aerospace Engineering
In Artificial Intelligence
- Yu Nishiyama
- Byron Boots
- Le Song
- Amir Hossein Afsharinejad
- Shunsuke Naruse
- Arthur Gretton
- Kenji Fukumizu
- Bernhard Schölkopf
Citations in the corpus
- The Nonparametric Kernel Bayes Smoother (2016, methodology): “Examples include the nonparametric kernel Bayes’ filter (nKB-filter) on discrete-time time-invariant state space models [10] and the Semiparametric kernel Monte-Carlo filter (KMC-filter) [16], as well as methods for dynamical system modeling and reinforcement learning applications [14, 21, 23, 4, 3,”