Generative Local Metric Learning for Nearest Neighbor Classification
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Noh, Yung-Kyun | - |
dc.contributor.author | Zhang, Byoung-Tak | - |
dc.contributor.author | Lee, Daniel D. | - |
dc.date.accessioned | 2022-07-12T17:11:27Z | - |
dc.date.available | 2022-07-12T17:11:27Z | - |
dc.date.created | 2021-05-14 | - |
dc.date.issued | 2018-01 | - |
dc.identifier.issn | 0162-8828 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/150664 | - |
dc.description.abstract | We consider the problem of learning a local metric in order to enhance the performance of nearest neighbor classification. Conventional metric learning methods attempt to separate data distributions in a purely discriminative manner; here we show how to take advantage of information from parametric generative models. We focus on the bias in the information-theoretic error arising from finite sampling effects, and find an appropriate local metric that maximally reduces the bias based upon knowledge from generative models. As a byproduct, the asymptotic theoretical analysis in this work relates metric learning to dimensionality reduction from a novel perspective, which was not understood from previous discriminative approaches. Empirical experiments show that this learned local metric enhances the discriminative nearest neighbor performance on various datasets using simple class conditional generative models such as a Gaussian. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE COMPUTER SOC | - |
dc.title | Generative Local Metric Learning for Nearest Neighbor Classification | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Noh, Yung-Kyun | - |
dc.identifier.doi | 10.1109/TPAMI.2017.2666151 | - |
dc.identifier.scopusid | 2-s2.0-85042592703 | - |
dc.identifier.wosid | 000417806000009 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, v.40, no.1, pp.106 - 118 | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | - |
dc.citation.title | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | - |
dc.citation.volume | 40 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 106 | - |
dc.citation.endPage | 118 | - |
dc.type.rims | ART | - |
dc.type.docType | 정기학술지(Article(Perspective Article포함)) | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science, Artificial IntelligenceEngineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Engineering | - |
dc.subject.keywordPlus | INFORMATION DISCRIMINANT-ANALYSIS | - |
dc.subject.keywordPlus | FEATURE-EXTRACTION | - |
dc.subject.keywordAuthor | Metric learning | - |
dc.subject.keywordAuthor | nearest neighbor classification | - |
dc.subject.keywordAuthor | f-divergence | - |
dc.subject.keywordAuthor | generative-discriminative hybridization | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/7847425 | - |
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