FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2893830906> ?p ?o ?g. }

• W2893830906 endingPage "517" @default.
• W2893830906 startingPage "510" @default.
• W2893830906 abstract "Constructing the adjacency graph is fundamental to graph-based clustering. Graph learning in kernel space has shown impressive performance on a number of benchmark data sets. However, its performance is largely determined by the chosen kernel matrix. To address this issue, previous multiple kernel learning algorithm has been applied to learn an optimal kernel from a group of predefined kernels. This approach might be sensitive to noise and limits the representation ability of the consensus kernel. In contrast to existing methods, we propose to learn a low-rank kernel matrix which exploits the similarity nature of the kernel matrix and seeks an optimal kernel from the neighborhood of candidate kernels. By formulating graph construction and kernel learning in a unified framework, the graph and consensus kernel can be iteratively enhanced by each other. Extensive experimental results validate the efficacy of the proposed method." @default.
• W2893830906 created "2018-10-05" @default.
• W2893830906 creator A5010510537 @default.
• W2893830906 creator A5042710020 @default.
• W2893830906 creator A5051227924 @default.
• W2893830906 creator A5052459660 @default.
• W2893830906 date "2019-01-01" @default.
• W2893830906 modified "2023-10-17" @default.
• W2893830906 title "Low-rank kernel learning for graph-based clustering" @default.
• W2893830906 cites W1680502250 @default.
• W2893830906 cites W1981838369 @default.
• W2893830906 cites W1992419399 @default.
• W2893830906 cites W1998871699 @default.
• W2893830906 cites W1999478155 @default.
• W2893830906 cites W201974436 @default.
• W2893830906 cites W2035979693 @default.
• W2893830906 cites W2044080809 @default.
• W2893830906 cites W2081549451 @default.
• W2893830906 cites W2115547324 @default.
• W2893830906 cites W2127615881 @default.
• W2893830906 cites W2132914434 @default.
• W2893830906 cites W2140095548 @default.
• W2893830906 cites W2141012957 @default.
• W2893830906 cites W2145962650 @default.
• W2893830906 cites W2153959628 @default.
• W2893830906 cites W2159091719 @default.
• W2893830906 cites W2168103112 @default.
• W2893830906 cites W2203392907 @default.
• W2893830906 cites W2581016781 @default.
• W2893830906 cites W2604114154 @default.
• W2893830906 cites W2611926148 @default.
• W2893830906 cites W2622869899 @default.
• W2893830906 cites W2744141159 @default.
• W2893830906 cites W2765771013 @default.
• W2893830906 cites W2789635684 @default.
• W2893830906 cites W2803104255 @default.
• W2893830906 cites W2803341107 @default.
• W2893830906 cites W2809034148 @default.
• W2893830906 cites W2963051348 @default.
• W2893830906 cites W2963089385 @default.
• W2893830906 cites W2964169082 @default.
• W2893830906 cites W3103583060 @default.
• W2893830906 cites W3105246522 @default.
• W2893830906 doi "https://doi.org/10.1016/j.knosys.2018.09.009" @default.
• W2893830906 hasPublicationYear "2019" @default.
• W2893830906 type Work @default.
• W2893830906 sameAs 2893830906 @default.
• W2893830906 citedByCount "129" @default.
• W2893830906 countsByYear W28938309062018 @default.
• W2893830906 countsByYear W28938309062019 @default.
• W2893830906 countsByYear W28938309062020 @default.
• W2893830906 countsByYear W28938309062021 @default.
• W2893830906 countsByYear W28938309062022 @default.
• W2893830906 countsByYear W28938309062023 @default.
• W2893830906 crossrefType "journal-article" @default.
• W2893830906 hasAuthorship W2893830906A5010510537 @default.
• W2893830906 hasAuthorship W2893830906A5042710020 @default.
• W2893830906 hasAuthorship W2893830906A5051227924 @default.
• W2893830906 hasAuthorship W2893830906A5052459660 @default.
• W2893830906 hasBestOaLocation W28938309062 @default.
• W2893830906 hasConcept C100595998 @default.
• W2893830906 hasConcept C118615104 @default.
• W2893830906 hasConcept C122280245 @default.
• W2893830906 hasConcept C12267149 @default.
• W2893830906 hasConcept C132525143 @default.
• W2893830906 hasConcept C134517425 @default.
• W2893830906 hasConcept C140417398 @default.
• W2893830906 hasConcept C154945302 @default.
• W2893830906 hasConcept C160446489 @default.
• W2893830906 hasConcept C180356752 @default.
• W2893830906 hasConcept C182335926 @default.
• W2893830906 hasConcept C195699287 @default.
• W2893830906 hasConcept C33923547 @default.
• W2893830906 hasConcept C41008148 @default.
• W2893830906 hasConcept C55851704 @default.
• W2893830906 hasConcept C73555534 @default.
• W2893830906 hasConcept C74193536 @default.
• W2893830906 hasConcept C75866337 @default.
• W2893830906 hasConcept C80444323 @default.
• W2893830906 hasConceptScore W2893830906C100595998 @default.
• W2893830906 hasConceptScore W2893830906C118615104 @default.
• W2893830906 hasConceptScore W2893830906C122280245 @default.
• W2893830906 hasConceptScore W2893830906C12267149 @default.
• W2893830906 hasConceptScore W2893830906C132525143 @default.
• W2893830906 hasConceptScore W2893830906C134517425 @default.
• W2893830906 hasConceptScore W2893830906C140417398 @default.
• W2893830906 hasConceptScore W2893830906C154945302 @default.
• W2893830906 hasConceptScore W2893830906C160446489 @default.
• W2893830906 hasConceptScore W2893830906C180356752 @default.
• W2893830906 hasConceptScore W2893830906C182335926 @default.
• W2893830906 hasConceptScore W2893830906C195699287 @default.
• W2893830906 hasConceptScore W2893830906C33923547 @default.
• W2893830906 hasConceptScore W2893830906C41008148 @default.
• W2893830906 hasConceptScore W2893830906C55851704 @default.
• W2893830906 hasConceptScore W2893830906C73555534 @default.
• W2893830906 hasConceptScore W2893830906C74193536 @default.
• W2893830906 hasConceptScore W2893830906C75866337 @default.
• W2893830906 hasConceptScore W2893830906C80444323 @default.

• next