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The hidden permutation model and location-based activity recognition

Bui, Hung H., Phung, Dinh, Venkatesh, Svetha and Phan, Hai 2008, The hidden permutation model and location-based activity recognition, in AAAI 2008 : Proceedings of the 23rd AAAI Conference on Artificial Intelligence, AAAI, [Chicago, Ill.], pp. 1345-1350.

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Title The hidden permutation model and location-based activity recognition
Author(s) Bui, Hung H.
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Phan, Hai
Conference name AAAI Conference on Artificial Intelligence (23rd : 2008 : Chicago, Ill.)
Conference location Chicago, Ill.
Conference dates 13-17 Jul. 2008
Title of proceedings AAAI 2008 : Proceedings of the 23rd AAAI Conference on Artificial Intelligence
Editor(s) [Unknown]
Publication date 2008
Conference series AAAI Conference on Artificial Intelligence
Start page 1345
End page 1350
Total pages 6
Publisher AAAI
Place of publication [Chicago, Ill.]
Keyword(s) applications
artificial intelligence
classifiers
distribution functions
learning systems
Markov processes
Summary Permutation modeling is challenging because of the combinatorial nature of the problem. However, such modeling is often required in many real-world applications, including activity recognition where subactivities are often permuted and partially ordered. This paper introduces a novel Hidden Permutation Model (HPM) that can learn the partial ordering constraints in permuted state sequences. The HPM is parameterized as an exponential family distribution and is flexible so that it can encode constraints via different feature functions. A chain-flipping Metropolis-Hastings Markov chain Monte Carlo (MCMC) is employed for inference to overcome the O(n!) complexity. Gradient-based maximum likelihood parameter learning is presented for two cases when the permutation is known and when it is hidden. The HPM is evaluated using both simulated and real data from a location-based activity recognition domain. Experimental results indicate that the HPM performs far better than other baseline models, including the naive Bayes classifier, the HMM classifier, and Kirshner's multinomial permutation model. Our presented HPM is generic and can potentially be utilized in any problem where the modeling of permuted states from noisy data is needed.
ISBN 9781577353683
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2008, American Association for Artificial Intelligence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044572

Document type: Conference Paper
Collection: School of Information Technology
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