{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/81093"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/81093","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Unified Discriminative Subspace Learning for Multimodality Image Analysis","abstract":"To demonstrate the effectiveness of the framework, an expert model of the query-driven locally adaptive (QDLA) method and four new subspace learning algorithms corresponding to different learning-locality criteria are presented. These four algorithms are locally embedded analysis (LEA), discriminant simplex analysis (DSA), correlation embedding analysis (CEA), and correlation tensor analysis (CTA). Extensive experiments demonstrate that applying the local manner in the sample space, feature space, and learning space can sufficiently boost the discriminating power for feature extraction by the subspace learning. As an advanced extension, a learning-locality based subspace learning algorithm for multiple/multimodality feature fusion is also developed in both unsupervised and supervised learning cases. Those methods are successfully applied to several real-world applications of facial image computing, such as face recognition, head pose estimation, realistic expression/emotion analysis, human age estimation, and lipreading.","abstract_html":"To demonstrate the effectiveness of the framework, an expert model of the query-driven locally adaptive (QDLA) method and four new subspace learning algorithms corresponding to different learning-locality criteria are presented. These four algorithms are locally embedded analysis (LEA), discriminant simplex analysis (DSA), correlation embedding analysis (CEA), and correlation tensor analysis (CTA). Extensive experiments demonstrate that applying the local manner in the sample space, feature space, and learning space can sufficiently boost the discriminating power for feature extraction by the subspace learning. As an advanced extension, a learning-locality based subspace learning algorithm for multiple/multimodality feature fusion is also developed in both unsupervised and supervised learning cases. Those methods are successfully applied to several real-world applications of facial image computing, such as face recognition, head pose estimation, realistic expression/emotion analysis, human age estimation, and lipreading.","abstract_has_math":false,"creators":["Fu, Yun"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":["Huang, Thomas S."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T20:09:33Z","date_published":"2015-09-25T20:09:33Z","updated_at":"2026-07-22T22:26:15Z","subjects":["Artificial Intelligence"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3337764"],"render_values":[{"text":"(MiAaPQ)AAI3337764","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/81093","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Huang, Thomas S."]},{"key":"dc:creator","label":"Author","values":["Fu, Yun"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T20:09:33Z","10000-01-01","2008"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Artificial Intelligence"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/81093","(MiAaPQ)AAI3337764"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["To demonstrate the effectiveness of the framework, an expert model of the query-driven locally adaptive (QDLA) method and four new subspace learning algorithms corresponding to different learning-locality criteria are presented. 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These four algorithms are locally embedded analysis (LEA), discriminant simplex analysis (DSA), correlation embedding analysis (CEA), and correlation tensor analysis (CTA). Extensive experiments demonstrate that applying the local manner in the sample space, feature space, and learning space can sufficiently boost the discriminating power for feature extraction by the subspace learning. As an advanced extension, a learning-locality based subspace learning algorithm for multiple/multimodality feature fusion is also developed in both unsupervised and supervised learning cases. Those methods are successfully applied to several real-world applications of facial image computing, such as face recognition, head pose estimation, realistic expression/emotion analysis, human age estimation, and lipreading.","Made available in DSpace on 2015-09-25T20:09:33Z (GMT). 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