2010 HilbertSpaceEmbeddingsofHiddenM

• (Song et al., 2010) ⇒ Le Song, Byron Boots, Sajid M. Siddiqi, Geoffrey Gordon, and Alex Smola. (2010). “Hilbert Space Embeddings of Hidden Markov Models.” In: Proceedings of the 27th International Conference on International Conference on Machine Learning. ISBN:978-1-60558-907-7

Subject Headings: Nonparametric HMM.

Cited By

• http://scholar.google.com/scholar?q=%222010%22+Hilbert+Space+Embeddings+of+Hidden+Markov+Models
• http://dl.acm.org/citation.cfm?id=3104322.3104448&preflayout=flat#citedby

Quotes

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 local-minimum-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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 HilbertSpaceEmbeddingsofHiddenM	Alexander J. Smola Geoffrey Gordon Le Song Byron Boots Sajid M. Siddiqi			Hilbert Space Embeddings of Hidden Markov Models						2010