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. Attached Files Name Description MIMEType Size Downloads Title The hidden permutation model and location-based activity recognition Author(s) Bui, Hung H. Phung, Dinh orcid.org/0000-0002-9977-8247 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 Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Fri, 20 Apr 2012, 11:33:02 EST Thu, 26 Apr 2012, 16:07:44 EST Thu, 26 Apr 2012, 16:10:04 EST Thu, 26 Apr 2012, 16:12:25 EST Mon, 14 May 2012, 09:39:35 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 10 times in Scopus Search Google Scholar Access Statistics: 532 Abstract Views, 4 File Downloads  -  Detailed Statistics Created: Fri, 20 Apr 2012, 11:33:02 EST

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