Loading…
Learning α-integration with partially-labeled data
Sensory data integration is an important task in human brain for multimodal processing as well as in machine learning for multisensor processing. α-integration was proposed by Amari as a principled way of blending multiple positive measures (e.g., stochastic models in the form of probability distrib...
Saved in:
Main Authors: | , , , |
---|---|
Format: | Conference Proceeding |
Language: | English |
Subjects: | |
Online Access: | Request full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | |
---|---|
cites | |
container_end_page | 2061 |
container_issue | |
container_start_page | 2058 |
container_title | |
container_volume | |
creator | Heeyoul Choi Seungjin Choi Katake, Anup Yoonsuck Choe |
description | Sensory data integration is an important task in human brain for multimodal processing as well as in machine learning for multisensor processing. α-integration was proposed by Amari as a principled way of blending multiple positive measures (e.g., stochastic models in the form of probability distributions), providing an optimal integration in the sense of minimizing the α-divergence. It also encompasses existing integration methods as its special case, e.g., weighted average and exponential mixture. In α-integration, the value of α determines the characteristics of the integration and the weight vector w assigns the degree of importance to each measure. In most of the existing work, however, α and w are given in advance rather than learned. In this paper we present two algorithms, for learning α and w from data when only a few integrated target values are available. Numerical experiments on synthetic as well as real-world data confirm the proposed method's effectiveness. |
doi_str_mv | 10.1109/ICASSP.2010.5495025 |
format | conference_proceeding |
fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_5495025</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5495025</ieee_id><sourcerecordid>5495025</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-b74af828411d9f9b25fad1f33a8aee23541e7322f2418e4deec6b87a728b5a0a3</originalsourceid><addsrcrecordid>eNpVj11KAzEUheMfONauoC-zgdTcm2SSPErRKgwoVMG3cse5qZFxLDMB6bLciGuyYF98OnA-OHxHiBmoOYAKV_eL69XqcY5qX1gTrEJ7JKbBeTBojMFQVceiQO2ChKBeTv4xG05FARaVrMCEc3Exju9KKe-ML4SumYY-9Zvy51umPvNmoJw--_Ir5bdyS0NO1HU72VHDHbdlS5kuxVmkbuTpISfi-fbmaXEn64fl3rSWCZzNsnGGokdvANoQQ4M2UgtRa_LEjNoaYKcRIxrwbFrm16rxjhz6xpIiPRGzv93EzOvtkD5o2K0P__UvARRLPw</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Learning α-integration with partially-labeled data</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Heeyoul Choi ; Seungjin Choi ; Katake, Anup ; Yoonsuck Choe</creator><creatorcontrib>Heeyoul Choi ; Seungjin Choi ; Katake, Anup ; Yoonsuck Choe</creatorcontrib><description>Sensory data integration is an important task in human brain for multimodal processing as well as in machine learning for multisensor processing. α-integration was proposed by Amari as a principled way of blending multiple positive measures (e.g., stochastic models in the form of probability distributions), providing an optimal integration in the sense of minimizing the α-divergence. It also encompasses existing integration methods as its special case, e.g., weighted average and exponential mixture. In α-integration, the value of α determines the characteristics of the integration and the weight vector w assigns the degree of importance to each measure. In most of the existing work, however, α and w are given in advance rather than learned. In this paper we present two algorithms, for learning α and w from data when only a few integrated target values are available. Numerical experiments on synthetic as well as real-world data confirm the proposed method's effectiveness.</description><identifier>ISSN: 1520-6149</identifier><identifier>ISBN: 9781424442959</identifier><identifier>ISBN: 1424442958</identifier><identifier>EISSN: 2379-190X</identifier><identifier>EISBN: 9781424442966</identifier><identifier>EISBN: 1424442966</identifier><identifier>DOI: 10.1109/ICASSP.2010.5495025</identifier><language>eng</language><publisher>IEEE</publisher><subject>Brain modeling ; Clustering algorithms ; Distributed computing ; Humans ; Inference algorithms ; Machine learning ; Parameter estimation ; Pattern recognition ; Probability distribution ; Stochastic processes ; α-integration</subject><ispartof>2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010, p.2058-2061</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5495025$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54555,54920,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5495025$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Heeyoul Choi</creatorcontrib><creatorcontrib>Seungjin Choi</creatorcontrib><creatorcontrib>Katake, Anup</creatorcontrib><creatorcontrib>Yoonsuck Choe</creatorcontrib><title>Learning α-integration with partially-labeled data</title><title>2010 IEEE International Conference on Acoustics, Speech and Signal Processing</title><addtitle>ICASSP</addtitle><description>Sensory data integration is an important task in human brain for multimodal processing as well as in machine learning for multisensor processing. α-integration was proposed by Amari as a principled way of blending multiple positive measures (e.g., stochastic models in the form of probability distributions), providing an optimal integration in the sense of minimizing the α-divergence. It also encompasses existing integration methods as its special case, e.g., weighted average and exponential mixture. In α-integration, the value of α determines the characteristics of the integration and the weight vector w assigns the degree of importance to each measure. In most of the existing work, however, α and w are given in advance rather than learned. In this paper we present two algorithms, for learning α and w from data when only a few integrated target values are available. Numerical experiments on synthetic as well as real-world data confirm the proposed method's effectiveness.</description><subject>Brain modeling</subject><subject>Clustering algorithms</subject><subject>Distributed computing</subject><subject>Humans</subject><subject>Inference algorithms</subject><subject>Machine learning</subject><subject>Parameter estimation</subject><subject>Pattern recognition</subject><subject>Probability distribution</subject><subject>Stochastic processes</subject><subject>α-integration</subject><issn>1520-6149</issn><issn>2379-190X</issn><isbn>9781424442959</isbn><isbn>1424442958</isbn><isbn>9781424442966</isbn><isbn>1424442966</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNpVj11KAzEUheMfONauoC-zgdTcm2SSPErRKgwoVMG3cse5qZFxLDMB6bLciGuyYF98OnA-OHxHiBmoOYAKV_eL69XqcY5qX1gTrEJ7JKbBeTBojMFQVceiQO2ChKBeTv4xG05FARaVrMCEc3Exju9KKe-ML4SumYY-9Zvy51umPvNmoJw--_Ir5bdyS0NO1HU72VHDHbdlS5kuxVmkbuTpISfi-fbmaXEn64fl3rSWCZzNsnGGokdvANoQQ4M2UgtRa_LEjNoaYKcRIxrwbFrm16rxjhz6xpIiPRGzv93EzOvtkD5o2K0P__UvARRLPw</recordid><startdate>201003</startdate><enddate>201003</enddate><creator>Heeyoul Choi</creator><creator>Seungjin Choi</creator><creator>Katake, Anup</creator><creator>Yoonsuck Choe</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201003</creationdate><title>Learning α-integration with partially-labeled data</title><author>Heeyoul Choi ; Seungjin Choi ; Katake, Anup ; Yoonsuck Choe</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-b74af828411d9f9b25fad1f33a8aee23541e7322f2418e4deec6b87a728b5a0a3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Brain modeling</topic><topic>Clustering algorithms</topic><topic>Distributed computing</topic><topic>Humans</topic><topic>Inference algorithms</topic><topic>Machine learning</topic><topic>Parameter estimation</topic><topic>Pattern recognition</topic><topic>Probability distribution</topic><topic>Stochastic processes</topic><topic>α-integration</topic><toplevel>online_resources</toplevel><creatorcontrib>Heeyoul Choi</creatorcontrib><creatorcontrib>Seungjin Choi</creatorcontrib><creatorcontrib>Katake, Anup</creatorcontrib><creatorcontrib>Yoonsuck Choe</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>Heeyoul Choi</au><au>Seungjin Choi</au><au>Katake, Anup</au><au>Yoonsuck Choe</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Learning α-integration with partially-labeled data</atitle><btitle>2010 IEEE International Conference on Acoustics, Speech and Signal Processing</btitle><stitle>ICASSP</stitle><date>2010-03</date><risdate>2010</risdate><spage>2058</spage><epage>2061</epage><pages>2058-2061</pages><issn>1520-6149</issn><eissn>2379-190X</eissn><isbn>9781424442959</isbn><isbn>1424442958</isbn><eisbn>9781424442966</eisbn><eisbn>1424442966</eisbn><abstract>Sensory data integration is an important task in human brain for multimodal processing as well as in machine learning for multisensor processing. α-integration was proposed by Amari as a principled way of blending multiple positive measures (e.g., stochastic models in the form of probability distributions), providing an optimal integration in the sense of minimizing the α-divergence. It also encompasses existing integration methods as its special case, e.g., weighted average and exponential mixture. In α-integration, the value of α determines the characteristics of the integration and the weight vector w assigns the degree of importance to each measure. In most of the existing work, however, α and w are given in advance rather than learned. In this paper we present two algorithms, for learning α and w from data when only a few integrated target values are available. Numerical experiments on synthetic as well as real-world data confirm the proposed method's effectiveness.</abstract><pub>IEEE</pub><doi>10.1109/ICASSP.2010.5495025</doi><tpages>4</tpages></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | ISSN: 1520-6149 |
ispartof | 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010, p.2058-2061 |
issn | 1520-6149 2379-190X |
language | eng |
recordid | cdi_ieee_primary_5495025 |
source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Brain modeling Clustering algorithms Distributed computing Humans Inference algorithms Machine learning Parameter estimation Pattern recognition Probability distribution Stochastic processes α-integration |
title | Learning α-integration with partially-labeled data |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T11%3A12%3A56IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Learning%20%CE%B1-integration%20with%20partially-labeled%20data&rft.btitle=2010%20IEEE%20International%20Conference%20on%20Acoustics,%20Speech%20and%20Signal%20Processing&rft.au=Heeyoul%20Choi&rft.date=2010-03&rft.spage=2058&rft.epage=2061&rft.pages=2058-2061&rft.issn=1520-6149&rft.eissn=2379-190X&rft.isbn=9781424442959&rft.isbn_list=1424442958&rft_id=info:doi/10.1109/ICASSP.2010.5495025&rft.eisbn=9781424442966&rft.eisbn_list=1424442966&rft_dat=%3Cieee_6IE%3E5495025%3C/ieee_6IE%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i175t-b74af828411d9f9b25fad1f33a8aee23541e7322f2418e4deec6b87a728b5a0a3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5495025&rfr_iscdi=true |