Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

article · Figshare · 2018

Hilbert Space Embeddings of Hidden Markov Models

Le Song, Byron Boots, Sajid M. Siddiqi, Geoffrey J. Gordon, Alex J. Smola

Cited by 160Open access

Abstract

Hidden Markov Models (HMMs) are important tools for modeling sequence data. However, they are restricted to discrete latent states, and are largely restricted to Gaussian and discrete observations. And, learning algorithms for HMMs have predominantly relied on local search heuristics, with the exception of spectral methods such as those described below. We propose a nonparametric HMM that extends traditional HMMs to structured and non-Gaussian continuous distributions. Furthermore, we derive a localminimum-free kernel spectral algorithm for learning these HMMs. We apply our method to robot vision data, slot car inertial sensor data and audio event classification data, and show that in these applications, embedded HMMs exceed the previous state-of-the-art performance.

Cite this paper

APA 7

Song, L., Boots, B., Siddiqi, S. M., Gordon, G. J., & Smola, A. J. (2018). Hilbert Space Embeddings of Hidden Markov Models. Figshare. https://doi.org/10.1184/r1/6475772

MLA 9

Song, Le, et al. “Hilbert Space Embeddings of Hidden Markov Models.” Figshare, 2018, https://doi.org/10.1184/r1/6475772.

Chicago

Song, Le, Byron Boots, Sajid M. Siddiqi, Geoffrey J. Gordon, and Alex J. Smola. 2018. “Hilbert Space Embeddings of Hidden Markov Models.” Figshare. https://doi.org/10.1184/r1/6475772.

Harvard

Song, L. et al. (2018) ‘Hilbert Space Embeddings of Hidden Markov Models’, Figshare. Available at: https://doi.org/10.1184/r1/6475772.

BibTeX
@article{song2018hilbert,
  title = {{Hilbert Space Embeddings of Hidden Markov Models}},
  author = {Song, Le and Boots, Byron and Siddiqi, Sajid M. and Gordon, Geoffrey J. and Smola, Alex J.},
  journal = {Figshare},
  year = {2018},
  doi = {10.1184/r1/6475772}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.

Related papers