FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3046336220> ?p ?o ?g. }

• W3046336220 endingPage "2058" @default.
• W3046336220 startingPage "2047" @default.
• W3046336220 abstract "The Kullback-Leibler divergence (KLD), which is widely used to measure the similarity between two distributions, plays an important role in many applications. In this article, we address the KLD metric-learning task, which aims at learning the best KLD-type metric from the distributions of datasets. Concretely, first, we extend the conventional KLD by introducing a linear mapping and obtain the best KLD to well express the similarity of data distributions by optimizing such a linear mapping. It improves the expressivity of data distribution, which means it makes the distributions in the same class close and those in different classes far away. Then, the KLD metric learning is modeled by a minimization problem on the manifold of all positive-definite matrices. To deal with this optimization task, we develop an intrinsic steepest descent method, which preserves the manifold structure of the metric in the iteration. Finally, we apply the proposed method along with ten popular metric-learning approaches on the tasks of 3-D object classification and document classification. The experimental results illustrate that our proposed method outperforms all other methods." @default.
• W3046336220 created "2020-08-07" @default.
• W3046336220 creator A5001543788 @default.
• W3046336220 creator A5024743253 @default.
• W3046336220 creator A5029698328 @default.
• W3046336220 creator A5063863772 @default.
• W3046336220 creator A5081489939 @default.
• W3046336220 creator A5085211629 @default.
• W3046336220 date "2022-04-01" @default.
• W3046336220 modified "2023-10-06" @default.
• W3046336220 title "Kullback–Leibler Divergence Metric Learning" @default.
• W3046336220 cites W1508195517 @default.
• W3046336220 cites W1644641054 @default.
• W3046336220 cites W1922045146 @default.
• W3046336220 cites W1928812244 @default.
• W3046336220 cites W1977193486 @default.
• W3046336220 cites W1986964250 @default.
• W3046336220 cites W1991469820 @default.
• W3046336220 cites W2001150023 @default.
• W3046336220 cites W2021122545 @default.
• W3046336220 cites W2032236594 @default.
• W3046336220 cites W2048997552 @default.
• W3046336220 cites W2064580901 @default.
• W3046336220 cites W2065675334 @default.
• W3046336220 cites W2073627450 @default.
• W3046336220 cites W2080067164 @default.
• W3046336220 cites W2120453412 @default.
• W3046336220 cites W2128559984 @default.
• W3046336220 cites W2132481658 @default.
• W3046336220 cites W2137894166 @default.
• W3046336220 cites W2143159002 @default.
• W3046336220 cites W2143668817 @default.
• W3046336220 cites W2147632348 @default.
• W3046336220 cites W2161685827 @default.
• W3046336220 cites W2163534628 @default.
• W3046336220 cites W2163584563 @default.
• W3046336220 cites W2164943005 @default.
• W3046336220 cites W2167581801 @default.
• W3046336220 cites W2169495281 @default.
• W3046336220 cites W2278012135 @default.
• W3046336220 cites W2413194904 @default.
• W3046336220 cites W2531468424 @default.
• W3046336220 cites W2555454054 @default.
• W3046336220 cites W2564853008 @default.
• W3046336220 cites W2615981376 @default.
• W3046336220 cites W2903531356 @default.
• W3046336220 cites W2991559096 @default.
• W3046336220 cites W3099172207 @default.
• W3046336220 cites W3148595793 @default.
• W3046336220 cites W4210880854 @default.
• W3046336220 cites W4298082496 @default.
• W3046336220 cites W4299345493 @default.
• W3046336220 doi "https://doi.org/10.1109/tcyb.2020.3008248" @default.
• W3046336220 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32721911" @default.
• W3046336220 hasPublicationYear "2022" @default.
• W3046336220 type Work @default.
• W3046336220 sameAs 3046336220 @default.
• W3046336220 citedByCount "18" @default.
• W3046336220 countsByYear W30463362202021 @default.
• W3046336220 countsByYear W30463362202022 @default.
• W3046336220 countsByYear W30463362202023 @default.
• W3046336220 crossrefType "journal-article" @default.
• W3046336220 hasAuthorship W3046336220A5001543788 @default.
• W3046336220 hasAuthorship W3046336220A5024743253 @default.
• W3046336220 hasAuthorship W3046336220A5029698328 @default.
• W3046336220 hasAuthorship W3046336220A5063863772 @default.
• W3046336220 hasAuthorship W3046336220A5081489939 @default.
• W3046336220 hasAuthorship W3046336220A5085211629 @default.
• W3046336220 hasConcept C103278499 @default.
• W3046336220 hasConcept C110121322 @default.
• W3046336220 hasConcept C115961682 @default.
• W3046336220 hasConcept C126255220 @default.
• W3046336220 hasConcept C127413603 @default.
• W3046336220 hasConcept C134306372 @default.
• W3046336220 hasConcept C138885662 @default.
• W3046336220 hasConcept C147764199 @default.
• W3046336220 hasConcept C153180895 @default.
• W3046336220 hasConcept C153258448 @default.
• W3046336220 hasConcept C154945302 @default.
• W3046336220 hasConcept C162324750 @default.
• W3046336220 hasConcept C171752962 @default.
• W3046336220 hasConcept C176217482 @default.
• W3046336220 hasConcept C207390915 @default.
• W3046336220 hasConcept C21547014 @default.
• W3046336220 hasConcept C2777212361 @default.
• W3046336220 hasConcept C33923547 @default.
• W3046336220 hasConcept C41008148 @default.
• W3046336220 hasConcept C41895202 @default.
• W3046336220 hasConcept C50644808 @default.
• W3046336220 hasConcept C529865628 @default.
• W3046336220 hasConcept C78519656 @default.
• W3046336220 hasConceptScore W3046336220C103278499 @default.
• W3046336220 hasConceptScore W3046336220C110121322 @default.
• W3046336220 hasConceptScore W3046336220C115961682 @default.
• W3046336220 hasConceptScore W3046336220C126255220 @default.
• W3046336220 hasConceptScore W3046336220C127413603 @default.
• W3046336220 hasConceptScore W3046336220C134306372 @default.
• W3046336220 hasConceptScore W3046336220C138885662 @default.

• next