Loading…

Reduction of Gaussian mixture models by maximum similarity

Scott and Szewczyk developed an iterative method to simplify (reduce the order of) a Gaussian mixture model by merging the two most similar components. Since the comparison of all pairs of components may not be feasible, they propose to consider only nearly adjacent components, with no guarantee tha...

Full description

Saved in:
Bibliographic Details
Published in:Journal of nonparametric statistics 2010-08, Vol.22 (6), p.703-709
Main Author: Harmse, Jørgen E.
Format: Article
Language:English
Subjects:
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c406t-6120e809e8c6600599a397ed39c5fafe63e1ea8bcad2e7e16987db93c3f58f1e3
cites
container_end_page 709
container_issue 6
container_start_page 703
container_title Journal of nonparametric statistics
container_volume 22
creator Harmse, Jørgen E.
description Scott and Szewczyk developed an iterative method to simplify (reduce the order of) a Gaussian mixture model by merging the two most similar components. Since the comparison of all pairs of components may not be feasible, they propose to consider only nearly adjacent components, with no guarantee that they find the most similar. I give a method to find the most similar pair of components without comparing all pairs, and I propose an extension to higher dimensions.
doi_str_mv 10.1080/10485250903377293
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_613769131</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2084963881</sourcerecordid><originalsourceid>FETCH-LOGICAL-c406t-6120e809e8c6600599a397ed39c5fafe63e1ea8bcad2e7e16987db93c3f58f1e3</originalsourceid><addsrcrecordid>eNqFkE1LxDAQhoso-PkDvBVPXqozzTYf4kXEL1gQRM8hm04gS9No0uLuv7fLelLE08zhed4Z3qI4RbhAkHCJMJNN3YACxoSoFdspDhBqVQFD3N3sM1lNQL1fHOa8BEDGGRwUVy_UjnbwsS-jKx_MmLM3fRn8ahgTlSG21OVysS6DWfkwhjL74DuT_LA-Lvac6TKdfM-j4u3-7vX2sZo_Pzzd3swrOwM-VBxrIAmKpOUcoFHKMCWoZco2zjjijJCMXFjT1iQIuZKiXShmmWukQ2JHxfk29z3Fj5HyoIPPlrrO9BTHrLHmXMwEcDmhZz_QZRxTP32nOTLBFTKcINxCNsWcEzn9nnwwaa0R9KZM_avMyRFbx_cupmA-Y-paPZh1F5NLprc-_7b0sBom8_pfk_19-At_JIt-</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>613769131</pqid></control><display><type>article</type><title>Reduction of Gaussian mixture models by maximum similarity</title><source>Taylor and Francis Science and Technology Collection</source><creator>Harmse, Jørgen E.</creator><creatorcontrib>Harmse, Jørgen E.</creatorcontrib><description>Scott and Szewczyk developed an iterative method to simplify (reduce the order of) a Gaussian mixture model by merging the two most similar components. Since the comparison of all pairs of components may not be feasible, they propose to consider only nearly adjacent components, with no guarantee that they find the most similar. I give a method to find the most similar pair of components without comparing all pairs, and I propose an extension to higher dimensions.</description><identifier>ISSN: 1048-5252</identifier><identifier>EISSN: 1029-0311</identifier><identifier>DOI: 10.1080/10485250903377293</identifier><language>eng</language><publisher>Abingdon: Taylor &amp; Francis</publisher><subject>60-04 ; 62-07 ; Algorithms ; Comparative analysis ; Estimating techniques ; Gaussian ; Gaussian mixture models ; Iterative methods ; kernel density estimation ; machine learning and neural networks ; Mathematical models ; Merging ; model simplification ; Nonparametric statistics ; Normal distribution ; Reduction ; Similarity</subject><ispartof>Journal of nonparametric statistics, 2010-08, Vol.22 (6), p.703-709</ispartof><rights>Copyright American Statistical Association and Taylor &amp; Francis 2010</rights><rights>Copyright Taylor &amp; Francis Ltd. Aug 2010</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c406t-6120e809e8c6600599a397ed39c5fafe63e1ea8bcad2e7e16987db93c3f58f1e3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Harmse, Jørgen E.</creatorcontrib><title>Reduction of Gaussian mixture models by maximum similarity</title><title>Journal of nonparametric statistics</title><description>Scott and Szewczyk developed an iterative method to simplify (reduce the order of) a Gaussian mixture model by merging the two most similar components. Since the comparison of all pairs of components may not be feasible, they propose to consider only nearly adjacent components, with no guarantee that they find the most similar. I give a method to find the most similar pair of components without comparing all pairs, and I propose an extension to higher dimensions.</description><subject>60-04</subject><subject>62-07</subject><subject>Algorithms</subject><subject>Comparative analysis</subject><subject>Estimating techniques</subject><subject>Gaussian</subject><subject>Gaussian mixture models</subject><subject>Iterative methods</subject><subject>kernel density estimation</subject><subject>machine learning and neural networks</subject><subject>Mathematical models</subject><subject>Merging</subject><subject>model simplification</subject><subject>Nonparametric statistics</subject><subject>Normal distribution</subject><subject>Reduction</subject><subject>Similarity</subject><issn>1048-5252</issn><issn>1029-0311</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><recordid>eNqFkE1LxDAQhoso-PkDvBVPXqozzTYf4kXEL1gQRM8hm04gS9No0uLuv7fLelLE08zhed4Z3qI4RbhAkHCJMJNN3YACxoSoFdspDhBqVQFD3N3sM1lNQL1fHOa8BEDGGRwUVy_UjnbwsS-jKx_MmLM3fRn8ahgTlSG21OVysS6DWfkwhjL74DuT_LA-Lvac6TKdfM-j4u3-7vX2sZo_Pzzd3swrOwM-VBxrIAmKpOUcoFHKMCWoZco2zjjijJCMXFjT1iQIuZKiXShmmWukQ2JHxfk29z3Fj5HyoIPPlrrO9BTHrLHmXMwEcDmhZz_QZRxTP32nOTLBFTKcINxCNsWcEzn9nnwwaa0R9KZM_avMyRFbx_cupmA-Y-paPZh1F5NLprc-_7b0sBom8_pfk_19-At_JIt-</recordid><startdate>201008</startdate><enddate>201008</enddate><creator>Harmse, Jørgen E.</creator><general>Taylor &amp; Francis</general><general>Taylor &amp; Francis Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>201008</creationdate><title>Reduction of Gaussian mixture models by maximum similarity</title><author>Harmse, Jørgen E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c406t-6120e809e8c6600599a397ed39c5fafe63e1ea8bcad2e7e16987db93c3f58f1e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>60-04</topic><topic>62-07</topic><topic>Algorithms</topic><topic>Comparative analysis</topic><topic>Estimating techniques</topic><topic>Gaussian</topic><topic>Gaussian mixture models</topic><topic>Iterative methods</topic><topic>kernel density estimation</topic><topic>machine learning and neural networks</topic><topic>Mathematical models</topic><topic>Merging</topic><topic>model simplification</topic><topic>Nonparametric statistics</topic><topic>Normal distribution</topic><topic>Reduction</topic><topic>Similarity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Harmse, Jørgen E.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Journal of nonparametric statistics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Harmse, Jørgen E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Reduction of Gaussian mixture models by maximum similarity</atitle><jtitle>Journal of nonparametric statistics</jtitle><date>2010-08</date><risdate>2010</risdate><volume>22</volume><issue>6</issue><spage>703</spage><epage>709</epage><pages>703-709</pages><issn>1048-5252</issn><eissn>1029-0311</eissn><abstract>Scott and Szewczyk developed an iterative method to simplify (reduce the order of) a Gaussian mixture model by merging the two most similar components. Since the comparison of all pairs of components may not be feasible, they propose to consider only nearly adjacent components, with no guarantee that they find the most similar. I give a method to find the most similar pair of components without comparing all pairs, and I propose an extension to higher dimensions.</abstract><cop>Abingdon</cop><pub>Taylor &amp; Francis</pub><doi>10.1080/10485250903377293</doi><tpages>7</tpages></addata></record>
fulltext fulltext
identifier ISSN: 1048-5252
ispartof Journal of nonparametric statistics, 2010-08, Vol.22 (6), p.703-709
issn 1048-5252
1029-0311
language eng
recordid cdi_proquest_journals_613769131
source Taylor and Francis Science and Technology Collection
subjects 60-04
62-07
Algorithms
Comparative analysis
Estimating techniques
Gaussian
Gaussian mixture models
Iterative methods
kernel density estimation
machine learning and neural networks
Mathematical models
Merging
model simplification
Nonparametric statistics
Normal distribution
Reduction
Similarity
title Reduction of Gaussian mixture models by maximum similarity
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-28T10%3A43%3A18IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Reduction%20of%20Gaussian%20mixture%20models%20by%20maximum%20similarity&rft.jtitle=Journal%20of%20nonparametric%20statistics&rft.au=Harmse,%20J%C3%B8rgen%20E.&rft.date=2010-08&rft.volume=22&rft.issue=6&rft.spage=703&rft.epage=709&rft.pages=703-709&rft.issn=1048-5252&rft.eissn=1029-0311&rft_id=info:doi/10.1080/10485250903377293&rft_dat=%3Cproquest_cross%3E2084963881%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c406t-6120e809e8c6600599a397ed39c5fafe63e1ea8bcad2e7e16987db93c3f58f1e3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=613769131&rft_id=info:pmid/&rfr_iscdi=true