Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Keywords |
|---|---|---|
2008 | AAAI | hmm classifier,multinomial permutation model,location-based activity recognition domain,naive bayes classifier,novel hidden permutation model,noisy data,permuted state,permutation modeling,hidden permutation model,activity recognition,permuted state sequence,markov processes,exponential family,partial order,artificial intelligence,maximum likelihood,applications,markov chain monte carlo,distribution functions,metropolis hastings |
Field | DocType | Citations |
Parameterized complexity,Markov process,Activity recognition,Markov chain Monte Carlo,Naive Bayes classifier,Pattern recognition,Computer science,Exponential family,Permutation,Artificial intelligence,Hidden Markov model,Machine learning | Conference | 5 |
PageRank | References | Authors |
0.53 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hung Hai Bui | 1 | 1188 | 112.37 |
| Dinh Q. Phung | 2 | 1469 | 144.58 |
| Svetha Venkatesh | 3 | 4190 | 425.27 |
| Hai Phan | 4 | 5 | 0.53 |