Consensus-based distributed expectation-maximization algorithm for density estimation and classification using wireless sensor networks
The present paper develops a decentralized expectation-maximization (EM) algorithm to estimate the parameters of a mixture density model for use in distributed learning tasks performed with data collected at spatially deployed wireless sensors. The E-step in the novel iterative scheme relies on loca...
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| container_end_page | 1992 |
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| container_start_page | 1989 |
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| creator | Forero, P.A. Cano, A. Giannakis, G.B. |
| description | The present paper develops a decentralized expectation-maximization (EM) algorithm to estimate the parameters of a mixture density model for use in distributed learning tasks performed with data collected at spatially deployed wireless sensors. The E-step in the novel iterative scheme relies on local information available to individual sensors, while during the M-step sensors exchange information only with their one- hop neighbors to reach consensus and eventually percolate the global information needed to estimate the wanted parameters across the wireless sensor network (WSN). Analysis and simulations demonstrate that the resultant consensus-based distributed EM (CB-DEM) algorithm matches well the resource- limited characteristics of WSNs and compares favorably with existing alternatives because it has wider applicability and remains resilient to inter-sensor communication noise. |
| doi_str_mv | 10.1109/ICASSP.2008.4518028 |
| format | Conference Proceeding |
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Analysis and simulations demonstrate that the resultant consensus-based distributed EM (CB-DEM) algorithm matches well the resource- limited characteristics of WSNs and compares favorably with existing alternatives because it has wider applicability and remains resilient to inter-sensor communication noise.</description><subject>Additive noise</subject><subject>Closed-form solution</subject><subject>Distributed Consensus</subject><subject>Distributed Estimation</subject><subject>Expectation-Maximization</subject><subject>Expectation-maximization algorithms</subject><subject>Gaussian noise</subject><subject>Local government</subject><subject>Maximum likelihood estimation</subject><subject>Mixture</subject><subject>Parameter estimation</subject><subject>Sensor Networks</subject><subject>Sensor phenomena and characterization</subject><subject>Statistical distributions</subject><subject>Wireless sensor networks</subject><issn>1520-6149</issn><issn>2379-190X</issn><isbn>9781424414833</isbn><isbn>1424414830</isbn><isbn>1424414849</isbn><isbn>9781424414840</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1UNtOAjEQrbdERL6Al_5Ase1O2fbREG8JiSZo4hvptrNY3QvZlgj-gL_tKjgvcz1n5gwhY8EnQnBz9TC7XiyeJpJzPQElNJf6iFwIkAACNJhjMpBZbpgw_PWEjEyu_3tZdkoGQknOpgLMORnF-M57A5Upowbke9Y2EZu4iaywET31IaYuFJvUx7hdo0s2hbZhtd2GOnz9JdRWq7YL6a2mZdtR3-ND2lGMKdSHgcZTV9kYQxncvrSJoVnRz9BhhTHS36U9tsH02XYf8ZKclbaKODr4IXm5vXme3bP5412vfs6CyFViHk3BrS6Md2UOWS-aWywAp6UBmEoHzkuHhS6c1LlS6FBLLFFZ8DkgqmxIxnvegIjLddcf3O2Wh59mP5-ibU8</recordid><startdate>200803</startdate><enddate>200803</enddate><creator>Forero, P.A.</creator><creator>Cano, A.</creator><creator>Giannakis, G.B.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200803</creationdate><title>Consensus-based distributed expectation-maximization algorithm for density estimation and classification using wireless sensor networks</title><author>Forero, P.A. ; Cano, A. ; Giannakis, G.B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-de9b0a8b9dcf7432370aeb4e6f94462c4cd2ceb8bc28755ece82efe5a4d74ee53</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Additive noise</topic><topic>Closed-form solution</topic><topic>Distributed Consensus</topic><topic>Distributed Estimation</topic><topic>Expectation-Maximization</topic><topic>Expectation-maximization algorithms</topic><topic>Gaussian noise</topic><topic>Local government</topic><topic>Maximum likelihood estimation</topic><topic>Mixture</topic><topic>Parameter estimation</topic><topic>Sensor Networks</topic><topic>Sensor phenomena and characterization</topic><topic>Statistical distributions</topic><topic>Wireless sensor networks</topic><toplevel>online_resources</toplevel><creatorcontrib>Forero, P.A.</creatorcontrib><creatorcontrib>Cano, A.</creatorcontrib><creatorcontrib>Giannakis, G.B.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Forero, P.A.</au><au>Cano, A.</au><au>Giannakis, G.B.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Consensus-based distributed expectation-maximization algorithm for density estimation and classification using wireless sensor networks</atitle><btitle>2008 IEEE International Conference on Acoustics, Speech and Signal Processing</btitle><stitle>ICASSP</stitle><date>2008-03</date><risdate>2008</risdate><spage>1989</spage><epage>1992</epage><pages>1989-1992</pages><issn>1520-6149</issn><eissn>2379-190X</eissn><isbn>9781424414833</isbn><isbn>1424414830</isbn><eisbn>1424414849</eisbn><eisbn>9781424414840</eisbn><abstract>The present paper develops a decentralized expectation-maximization (EM) algorithm to estimate the parameters of a mixture density model for use in distributed learning tasks performed with data collected at spatially deployed wireless sensors. 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| ispartof | 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008, p.1989-1992 |
| issn | 1520-6149 2379-190X |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Additive noise Closed-form solution Distributed Consensus Distributed Estimation Expectation-Maximization Expectation-maximization algorithms Gaussian noise Local government Maximum likelihood estimation Mixture Parameter estimation Sensor Networks Sensor phenomena and characterization Statistical distributions Wireless sensor networks |
| title | Consensus-based distributed expectation-maximization algorithm for density estimation and classification using wireless sensor networks |
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