Approximating a non-homogeneous HMM with Dynamic Spatial Dirichlet Process
In this work we present a model that uses a Dirichlet process (DP) with a dynamic spatial constraints to approximate a non-homogeneous hidden Markov model (NHMM). The coefficient of the spatial constraint, which is locally dependent on each site, modulates the time-variant transition probability mat...
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| creator | Ren, H. Liang Wu Neskovic, P. Cooper, L. |
| description | In this work we present a model that uses a Dirichlet process (DP) with a dynamic spatial constraints to approximate a non-homogeneous hidden Markov model (NHMM). The coefficient of the spatial constraint, which is locally dependent on each site, modulates the time-variant transition probability matrix. In our model, we use the DP in combination with variational Bayesian inference to estimate the local coefficients and the time-dependent structure of the hidden states. In addition, the formulation of the NHMM within the DP framework does not require the specification of the number of states. Our results demonstrate that the proposed model can uncover the hidden states when the observed data is generated by a NHMM model and the number of hidden states is unknown. |
| doi_str_mv | 10.1109/ICPR.2008.4761919 |
| format | Conference Proceeding |
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Our results demonstrate that the proposed model can uncover the hidden states when the observed data is generated by a NHMM model and the number of hidden states is unknown.</description><subject>Bayesian methods</subject><subject>Brain modeling</subject><subject>Educational institutions</subject><subject>Handwriting recognition</subject><subject>Hidden Markov models</subject><subject>Inference algorithms</subject><subject>Physics</subject><subject>Software engineering</subject><subject>Speech</subject><subject>State estimation</subject><issn>1051-4651</issn><issn>2831-7475</issn><isbn>9781424421749</isbn><isbn>1424421748</isbn><isbn>9781424421756</isbn><isbn>1424421756</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVkMtOwzAURM1LIpR-AGLjH0i414_EXlZtoUWtqKD7ykmcxigvxUHQvycS3bCaxYyOjoaQB4QIEfTTer57jxiAikQSo0Z9QaY6USiYEAwTGV-SgCmOYSISefWvE_qaBAgSQxFLvCV33n8CMOBSBeR11nV9--NqM7jmSA1t2iYs27o92sa2X56utlv67YaSLk6NqV1GP7pxaiq6cL3LysoOdNe3mfX-ntwUpvJ2es4J2T8v9_NVuHl7Wc9nm9BpGEKBTEICKYciBiVHPcNULrK84CpLJZMqh0xbVBJSwQVoCwUHZo3WlhkAPiGPf1hnrT10_ajenw7nV_gvCblQbA</recordid><startdate>200812</startdate><enddate>200812</enddate><creator>Ren, H.</creator><creator>Liang Wu</creator><creator>Neskovic, P.</creator><creator>Cooper, L.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200812</creationdate><title>Approximating a non-homogeneous HMM with Dynamic Spatial Dirichlet Process</title><author>Ren, H. ; Liang Wu ; Neskovic, P. ; Cooper, L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-4125070b30f6085174a28d4cdf38cb5258d0c9e1850b43409e0f302ea99e2a003</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Bayesian methods</topic><topic>Brain modeling</topic><topic>Educational institutions</topic><topic>Handwriting recognition</topic><topic>Hidden Markov models</topic><topic>Inference algorithms</topic><topic>Physics</topic><topic>Software engineering</topic><topic>Speech</topic><topic>State estimation</topic><toplevel>online_resources</toplevel><creatorcontrib>Ren, H.</creatorcontrib><creatorcontrib>Liang Wu</creatorcontrib><creatorcontrib>Neskovic, P.</creatorcontrib><creatorcontrib>Cooper, L.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ren, H.</au><au>Liang Wu</au><au>Neskovic, P.</au><au>Cooper, L.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Approximating a non-homogeneous HMM with Dynamic Spatial Dirichlet Process</atitle><btitle>2008 19th International Conference on Pattern Recognition</btitle><stitle>ICPR</stitle><date>2008-12</date><risdate>2008</risdate><spage>1</spage><epage>4</epage><pages>1-4</pages><issn>1051-4651</issn><eissn>2831-7475</eissn><isbn>9781424421749</isbn><isbn>1424421748</isbn><eisbn>9781424421756</eisbn><eisbn>1424421756</eisbn><abstract>In this work we present a model that uses a Dirichlet process (DP) with a dynamic spatial constraints to approximate a non-homogeneous hidden Markov model (NHMM). The coefficient of the spatial constraint, which is locally dependent on each site, modulates the time-variant transition probability matrix. In our model, we use the DP in combination with variational Bayesian inference to estimate the local coefficients and the time-dependent structure of the hidden states. In addition, the formulation of the NHMM within the DP framework does not require the specification of the number of states. Our results demonstrate that the proposed model can uncover the hidden states when the observed data is generated by a NHMM model and the number of hidden states is unknown.</abstract><pub>IEEE</pub><doi>10.1109/ICPR.2008.4761919</doi><tpages>4</tpages></addata></record> |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Bayesian methods Brain modeling Educational institutions Handwriting recognition Hidden Markov models Inference algorithms Physics Software engineering Speech State estimation |
| title | Approximating a non-homogeneous HMM with Dynamic Spatial Dirichlet Process |
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