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dc.contributor.authorKolter, Jeremy Z.
dc.contributor.authorJaakkola, Tommi S.
dc.date.accessioned2018-05-11T17:10:28Z
dc.date.available2018-05-11T17:10:28Z
dc.date.issued2012-04
dc.identifier.urihttp://hdl.handle.net/1721.1/115326
dc.description.abstractThis paper considers additive factorial hidden Markov models, an extension to HMMs where the state factors into multiple independent chains, and the output is an additive function of all the hidden states. Although such models are very powerful, accurate inference is unfortunately difficult: exact inference is not computationally tractable, and existing approximate inference techniques are highly susceptible to local optima. In this paper we propose an alternative inference method for such models, which exploits their additive structure by 1) looking at the observed difference signal of the observation, 2) incorporating a “robust” mixture component that can account for unmodeled observations, and 3) constraining the posterior to allow at most one hidden state to change at a time. Combining these elements we develop a convex formulation of approximate inference that is computationally efficient, has no issues of local optima, and which performs much better than existing approaches in practice. The method is motivated by the problem of energy disaggregation, the task of taking a whole home electricity signal and decomposing it into its component appliances; applied to this task, our algorithm achieves state-of-the-art performance, and is able to separate many appliances almost perfectly using just the total aggregate signal.en_US
dc.language.isoen_US
dc.publisherProceedings of Machine Learning Researchen_US
dc.relation.isversionofhttp://proceedings.mlr.press/v22/zico12.htmlen_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceOther univ. web domainen_US
dc.titleApproximate inference in additive factorial HMMs with application to energy disaggregationen_US
dc.typeArticleen_US
dc.identifier.citationKolter, J. Zico and Tommi Jaakkola. "Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation." Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 21-23 April, 2012, La Palma, Canary Islands, PMLR, 2012.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.mitauthorKolter, Jeremy Z.
dc.contributor.mitauthorJaakkola, Tommi S.
dc.relation.journalProceedings of the Fifteenth International Conference on Artificial Intelligence and Statisticsen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dspace.orderedauthorsKolter, J. Zico; Jaakkola, Tommien_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0002-2199-0379
mit.licenseOPEN_ACCESS_POLICYen_US


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