Monte Carlo Filtering Using Kernel Embedding of Distributions

Motonobu Kanagawa, Yu Nishiyama, Arthur Gretton, Kenji Fukumizu

AAAI 2014 pp. 1897-1903

doi:10.1609/AAAI.V28I1.8984 /aaai/2014/kanagawa2014aaai-monte/

Abstract

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.

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Text

Kanagawa et al. "Monte Carlo Filtering Using Kernel Embedding of Distributions." AAAI Conference on Artificial Intelligence, 2014. doi:10.1609/AAAI.V28I1.8984

Markdown

[Kanagawa et al. "Monte Carlo Filtering Using Kernel Embedding of Distributions." AAAI Conference on Artificial Intelligence, 2014.](https://mlanthology.org/aaai/2014/kanagawa2014aaai-monte/) doi:10.1609/AAAI.V28I1.8984

BibTeX

@inproceedings{kanagawa2014aaai-monte,
  title     = {{Monte Carlo Filtering Using Kernel Embedding of Distributions}},
  author    = {Kanagawa, Motonobu and Nishiyama, Yu and Gretton, Arthur and Fukumizu, Kenji},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2014},
  pages     = {1897-1903},
  doi       = {10.1609/AAAI.V28I1.8984},
  url       = {https://mlanthology.org/aaai/2014/kanagawa2014aaai-monte/}
}