FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2137201712> ?p ?o ?g. }

• W2137201712 endingPage "1367" @default.
• W2137201712 startingPage "1359" @default.
• W2137201712 abstract "Classical methods such as Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) are ubiquitous in statistics. However, these techniques are only able to reveal linear relationships in data. Although nonlinear variants of PCA and CCA have been proposed, these are computationally prohibitive in the large scale.In a separate strand of recent research, randomized methods have been proposed to construct features that help reveal nonlinear patterns in data. For basic tasks such as regression or classification, random features exhibit little or no loss in performance, while achieving drastic savings in computational requirements.In this paper we leverage randomness to design scalable new variants of nonlinear PCA and CCA; our ideas extend to key multivariate analysis tools such as spectral clustering or LDA. We demonstrate our algorithms through experiments on real-world data, on which we compare against the state-of-the-art. A simple R implementation of the presented algorithms is provided." @default.
• W2137201712 created "2016-06-24" @default.
• W2137201712 creator A5010820865 @default.
• W2137201712 creator A5016930800 @default.
• W2137201712 creator A5058767558 @default.
• W2137201712 creator A5087642472 @default.
• W2137201712 creator A5090351022 @default.
• W2137201712 date "2014-06-21" @default.
• W2137201712 modified "2023-10-12" @default.
• W2137201712 title "Randomized Nonlinear Component Analysis" @default.
• W2137201712 cites W120847089 @default.
• W2137201712 cites W1523385540 @default.
• W2137201712 cites W1534121600 @default.
• W2137201712 cites W1536675765 @default.
• W2137201712 cites W1560724230 @default.
• W2137201712 cites W1999352252 @default.
• W2137201712 cites W2025341678 @default.
• W2137201712 cites W2047811252 @default.
• W2137201712 cites W2067562626 @default.
• W2137201712 cites W2078626246 @default.
• W2137201712 cites W2095841525 @default.
• W2137201712 cites W2100039146 @default.
• W2137201712 cites W2100495367 @default.
• W2137201712 cites W2105527258 @default.
• W2137201712 cites W2107791152 @default.
• W2137201712 cites W2112545207 @default.
• W2137201712 cites W2118563516 @default.
• W2137201712 cites W2123395972 @default.
• W2137201712 cites W2124101779 @default.
• W2137201712 cites W2130055251 @default.
• W2137201712 cites W2132914434 @default.
• W2137201712 cites W2134270519 @default.
• W2137201712 cites W2142674578 @default.
• W2137201712 cites W2144414190 @default.
• W2137201712 cites W2147118250 @default.
• W2137201712 cites W2162892577 @default.
• W2137201712 cites W2294798173 @default.
• W2137201712 cites W2405732242 @default.
• W2137201712 cites W2962910688 @default.
• W2137201712 cites W2964265625 @default.
• W2137201712 cites W3002694247 @default.
• W2137201712 cites W3099514962 @default.
• W2137201712 cites W3023820860 @default.
• W2137201712 hasPublicationYear "2014" @default.
• W2137201712 type Work @default.
• W2137201712 sameAs 2137201712 @default.
• W2137201712 citedByCount "33" @default.
• W2137201712 countsByYear W21372017122014 @default.
• W2137201712 countsByYear W21372017122015 @default.
• W2137201712 countsByYear W21372017122016 @default.
• W2137201712 countsByYear W21372017122017 @default.
• W2137201712 countsByYear W21372017122019 @default.
• W2137201712 countsByYear W21372017122020 @default.
• W2137201712 countsByYear W21372017122021 @default.
• W2137201712 crossrefType "proceedings-article" @default.
• W2137201712 hasAuthorship W2137201712A5010820865 @default.
• W2137201712 hasAuthorship W2137201712A5016930800 @default.
• W2137201712 hasAuthorship W2137201712A5058767558 @default.
• W2137201712 hasAuthorship W2137201712A5087642472 @default.
• W2137201712 hasAuthorship W2137201712A5090351022 @default.
• W2137201712 hasConcept C105795698 @default.
• W2137201712 hasConcept C119857082 @default.
• W2137201712 hasConcept C121332964 @default.
• W2137201712 hasConcept C124101348 @default.
• W2137201712 hasConcept C125112378 @default.
• W2137201712 hasConcept C153083717 @default.
• W2137201712 hasConcept C153180895 @default.
• W2137201712 hasConcept C153874254 @default.
• W2137201712 hasConcept C154945302 @default.
• W2137201712 hasConcept C158622935 @default.
• W2137201712 hasConcept C161584116 @default.
• W2137201712 hasConcept C27438332 @default.
• W2137201712 hasConcept C2777749129 @default.
• W2137201712 hasConcept C33923547 @default.
• W2137201712 hasConcept C41008148 @default.
• W2137201712 hasConcept C48044578 @default.
• W2137201712 hasConcept C51432778 @default.
• W2137201712 hasConcept C62520636 @default.
• W2137201712 hasConcept C73555534 @default.
• W2137201712 hasConcept C77088390 @default.
• W2137201712 hasConceptScore W2137201712C105795698 @default.
• W2137201712 hasConceptScore W2137201712C119857082 @default.
• W2137201712 hasConceptScore W2137201712C121332964 @default.
• W2137201712 hasConceptScore W2137201712C124101348 @default.
• W2137201712 hasConceptScore W2137201712C125112378 @default.
• W2137201712 hasConceptScore W2137201712C153083717 @default.
• W2137201712 hasConceptScore W2137201712C153180895 @default.
• W2137201712 hasConceptScore W2137201712C153874254 @default.
• W2137201712 hasConceptScore W2137201712C154945302 @default.
• W2137201712 hasConceptScore W2137201712C158622935 @default.
• W2137201712 hasConceptScore W2137201712C161584116 @default.
• W2137201712 hasConceptScore W2137201712C27438332 @default.
• W2137201712 hasConceptScore W2137201712C2777749129 @default.
• W2137201712 hasConceptScore W2137201712C33923547 @default.
• W2137201712 hasConceptScore W2137201712C41008148 @default.
• W2137201712 hasConceptScore W2137201712C48044578 @default.
• W2137201712 hasConceptScore W2137201712C51432778 @default.
• W2137201712 hasConceptScore W2137201712C62520636 @default.

• next