Dimension Reduction With Prior Information for Knowledge Discovery
This paper addresses the problem of mapping high-dimensional data to a low-dimensional space, in the presence of other known features. This problem is ubiquitous in science and engineering as there are often controllable/measurable features in most applications. To solve this problem, this paper pro...
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| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence 2024-05, Vol.46 (5), p.3625-3636 |
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| container_title | IEEE transactions on pattern analysis and machine intelligence |
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| creator | Bui, Anh Tuan |
| description | This paper addresses the problem of mapping high-dimensional data to a low-dimensional space, in the presence of other known features. This problem is ubiquitous in science and engineering as there are often controllable/measurable features in most applications. To solve this problem, this paper proposes a broad class of methods, which is referred to as conditional multidimensional scaling (MDS). An algorithm for optimizing the objective function of conditional MDS is also developed. The convergence of this algorithm is proven under mild assumptions. Conditional MDS is illustrated with kinship terms, facial expressions, textile fabrics, car-brand perception, and cylinder machining examples. These examples demonstrate the advantages of conditional MDS over conventional dimension reduction in improving the estimation quality of the reduced-dimension space and simplifying visualization and knowledge discovery tasks. Computer codes for this work are available in the open-source cml R package. |
| doi_str_mv | 10.1109/TPAMI.2023.3346212 |
| format | Article |
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This problem is ubiquitous in science and engineering as there are often controllable/measurable features in most applications. To solve this problem, this paper proposes a broad class of methods, which is referred to as conditional multidimensional scaling (MDS). An algorithm for optimizing the objective function of conditional MDS is also developed. The convergence of this algorithm is proven under mild assumptions. Conditional MDS is illustrated with kinship terms, facial expressions, textile fabrics, car-brand perception, and cylinder machining examples. These examples demonstrate the advantages of conditional MDS over conventional dimension reduction in improving the estimation quality of the reduced-dimension space and simplifying visualization and knowledge discovery tasks. 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This problem is ubiquitous in science and engineering as there are often controllable/measurable features in most applications. To solve this problem, this paper proposes a broad class of methods, which is referred to as conditional multidimensional scaling (MDS). An algorithm for optimizing the objective function of conditional MDS is also developed. The convergence of this algorithm is proven under mild assumptions. Conditional MDS is illustrated with kinship terms, facial expressions, textile fabrics, car-brand perception, and cylinder machining examples. These examples demonstrate the advantages of conditional MDS over conventional dimension reduction in improving the estimation quality of the reduced-dimension space and simplifying visualization and knowledge discovery tasks. Computer codes for this work are available in the open-source cml R package.</description><subject>Algorithms</subject><subject>Controllability</subject><subject>Data visualization</subject><subject>Dimensionality reduction</subject><subject>Distance scaling</subject><subject>ISOMAP</subject><subject>Knowledge discovery</subject><subject>Machining</subject><subject>Manifolds</subject><subject>Measurement</subject><subject>Multidimensional methods</subject><subject>multidimensional scaling</subject><subject>Principal component analysis</subject><subject>Reduction</subject><subject>Sammon mapping</subject><subject>SMACOF</subject><subject>Task analysis</subject><issn>0162-8828</issn><issn>1939-3539</issn><issn>2160-9292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkN9LwzAQx4Mobk7_AREp-OJLZ5Jr0-Rxbv4YThwy8bG06VU71mYmq7L_3nabIj7dcfncN8eHkFNG-4xRdTWbDh7HfU459AECwRnfI12mQPkQgtonXcoE96XkskOOnJtTyoKQwiHpgAyFjCLZJdejosTKFabynjGr9artXovVuze1hbHeuMqNLZPNuOm8h8p8LTB7Q29UOG0-0a6PyUGeLBye7GqPvNzezIb3_uTpbjwcTHwNFFa-FogplwHXqQ6ozqUONU0EJJilLMojRZUKBTIhApGxNMI0SDmHINM0y6lG6JHLbe7Smo8a3SoumxNwsUgqNLWLm1-AMsqYbNCLf-jc1LZqrmspBiqQqqX4ltLWOGcxj5e2KBO7jhmNW8PxxnDcGo53hpul8110nZaY_a78KG2Asy1QIOKfRIhY8wrf7G5_bA</recordid><startdate>20240501</startdate><enddate>20240501</enddate><creator>Bui, Anh Tuan</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-0397-1307</orcidid></search><sort><creationdate>20240501</creationdate><title>Dimension Reduction With Prior Information for Knowledge Discovery</title><author>Bui, Anh Tuan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c303t-c6eeb2842cbc40cf8c5c0a63aedb17f7909956e16646d1b7eb4b2234dc0df0ce3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Controllability</topic><topic>Data visualization</topic><topic>Dimensionality reduction</topic><topic>Distance scaling</topic><topic>ISOMAP</topic><topic>Knowledge discovery</topic><topic>Machining</topic><topic>Manifolds</topic><topic>Measurement</topic><topic>Multidimensional methods</topic><topic>multidimensional scaling</topic><topic>Principal component analysis</topic><topic>Reduction</topic><topic>Sammon mapping</topic><topic>SMACOF</topic><topic>Task analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bui, Anh Tuan</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998–Present</collection><collection>IEEE/IET Electronic Library</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications 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><collection>MEDLINE - Academic</collection><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Bui, Anh Tuan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dimension Reduction With Prior Information for Knowledge Discovery</atitle><jtitle>IEEE transactions on pattern analysis and machine intelligence</jtitle><stitle>TPAMI</stitle><addtitle>IEEE Trans Pattern Anal Mach Intell</addtitle><date>2024-05-01</date><risdate>2024</risdate><volume>46</volume><issue>5</issue><spage>3625</spage><epage>3636</epage><pages>3625-3636</pages><issn>0162-8828</issn><eissn>1939-3539</eissn><eissn>2160-9292</eissn><coden>ITPIDJ</coden><abstract>This paper addresses the problem of mapping high-dimensional data to a low-dimensional space, in the presence of other known features. This problem is ubiquitous in science and engineering as there are often controllable/measurable features in most applications. To solve this problem, this paper proposes a broad class of methods, which is referred to as conditional multidimensional scaling (MDS). An algorithm for optimizing the objective function of conditional MDS is also developed. The convergence of this algorithm is proven under mild assumptions. Conditional MDS is illustrated with kinship terms, facial expressions, textile fabrics, car-brand perception, and cylinder machining examples. These examples demonstrate the advantages of conditional MDS over conventional dimension reduction in improving the estimation quality of the reduced-dimension space and simplifying visualization and knowledge discovery tasks. 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| ispartof | IEEE transactions on pattern analysis and machine intelligence, 2024-05, Vol.46 (5), p.3625-3636 |
| issn | 0162-8828 1939-3539 2160-9292 |
| language | eng |
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| source | IEEE/IET Electronic Library |
| subjects | Algorithms Controllability Data visualization Dimensionality reduction Distance scaling ISOMAP Knowledge discovery Machining Manifolds Measurement Multidimensional methods multidimensional scaling Principal component analysis Reduction Sammon mapping SMACOF Task analysis |
| title | Dimension Reduction With Prior Information for Knowledge Discovery |
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