Monte Carlo Filtering Using Kernel Embedding of Distributions


  • Motonobu Kanagawa Graduate University for Advanced Studies
  • Yu Nishiyama The Institute of Statistical Mathematics
  • Arthur Gretton University College London
  • Kenji Fukumizu The Institute of Statistical Mathematics



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.




How to Cite

Kanagawa, M., Nishiyama, Y., Gretton, A., & Fukumizu, K. (2014). Monte Carlo Filtering Using Kernel Embedding of Distributions. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1).



Main Track: Novel Machine Learning Algorithms