FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2606536620> ?p ?o ?g. }

• W2606536620 endingPage "327" @default.
• W2606536620 startingPage "322" @default.
• W2606536620 abstract "Abstract—Canonical correlation analysis (CCA) is a multi-variate statistical technique for finding the linear relationshipbetween two sets of variables. The kernel generalization ofCCA named kernel CCA has been proposed to find nonlinearrelations between data sets. Despite the wide usage of CCAand kernel CCA, they have one common limitation that is thelack of sparsity in their solution. In this paper, we considersparse kernel CCA and propose a novel sparse kernel CCAalgorithm (SKCCA). Our algorithm is based on a relationshipbetween kernel CCA and least squares. Sparsity of the dualtransformations is introduced by penalizing the ‘ 1 -norm of dualvectors. Experiments demonstrate that our algorithm not onlyperforms well in computing sparse dual transformations butalso can alleviate the over-fitting problem of kernel CCA.Index Terms—canonical correlation analysis, kernel, sparsity I. I NTRODUCTION T HE description of relationship between two sets ofvariables has long been an interesting topic to manyresearchers. Canonical correlation analysis (CCA) [10] isa multivariate statistical technique for finding the linearrelationship between two sets of variables. It seeks a lineartransformation for each of the two sets of variables in away that the projected variables in the transformed spaceare maximally correlated. In recent years, CCA has beensuccessfully applied in various areas, including genomicdata analysis [19], [20] and bilingual analysis [18], whereresearchers can measure multiple sets of variables on a singlesubject. For instance, DNA copy number variations, geneexpression, and single nucleotide polymorphism (SNP) datamight all be available on a common set of patient samples.Since CCA only consider linear transformation of theoriginal variables, it fails to capture nonlinear relations. How-ever, in a wide range of practical problems linear relationsmay not be adequate for studying relation among variables.Detecting nonlinear relations among data is important anduseful in modern data analysis, especially when dealingwith data that are not in the form of vectors, such as textdocuments, images, microarray data and so on. A naturalextension, therefore, is to explore and exploit nonlinear rela-tions among data. Among nonlinear extensions of CCA, onemost frequently used approach is the kernel generalization" @default.
• W2606536620 created "2017-04-28" @default.
• W2606536620 creator A5022387395 @default.
• W2606536620 creator A5041423958 @default.
• W2606536620 creator A5068441236 @default.
• W2606536620 creator A5079973146 @default.
• W2606536620 date "2013-01-01" @default.
• W2606536620 modified "2023-09-27" @default.
• W2606536620 title "Sparse Kernel Canonical Correlation Analysis" @default.
• W2606536620 cites W1497960466 @default.
• W2606536620 cites W1560724230 @default.
• W2606536620 cites W158687851 @default.
• W2606536620 cites W1663973292 @default.
• W2606536620 cites W1984983329 @default.
• W2606536620 cites W1986280275 @default.
• W2606536620 cites W2008929650 @default.
• W2606536620 cites W2009702064 @default.
• W2606536620 cites W2025341678 @default.
• W2606536620 cites W2047631354 @default.
• W2606536620 cites W2076455317 @default.
• W2606536620 cites W2098262342 @default.
• W2606536620 cites W2098290597 @default.
• W2606536620 cites W2100235303 @default.
• W2606536620 cites W2106554478 @default.
• W2606536620 cites W2108659514 @default.
• W2606536620 cites W2124101779 @default.
• W2606536620 cites W2125972593 @default.
• W2606536620 cites W2135046866 @default.
• W2606536620 cites W2140095548 @default.
• W2606536620 cites W2163771264 @default.
• W2606536620 hasPublicationYear "2013" @default.
• W2606536620 type Work @default.
• W2606536620 sameAs 2606536620 @default.
• W2606536620 citedByCount "7" @default.
• W2606536620 countsByYear W26065366202017 @default.
• W2606536620 countsByYear W26065366202018 @default.
• W2606536620 countsByYear W26065366202019 @default.
• W2606536620 crossrefType "proceedings-article" @default.
• W2606536620 hasAuthorship W2606536620A5022387395 @default.
• W2606536620 hasAuthorship W2606536620A5041423958 @default.
• W2606536620 hasAuthorship W2606536620A5068441236 @default.
• W2606536620 hasAuthorship W2606536620A5079973146 @default.
• W2606536620 hasConcept C104317684 @default.
• W2606536620 hasConcept C105795698 @default.
• W2606536620 hasConcept C118615104 @default.
• W2606536620 hasConcept C122280245 @default.
• W2606536620 hasConcept C12267149 @default.
• W2606536620 hasConcept C153180895 @default.
• W2606536620 hasConcept C153874254 @default.
• W2606536620 hasConcept C154945302 @default.
• W2606536620 hasConcept C182335926 @default.
• W2606536620 hasConcept C185592680 @default.
• W2606536620 hasConcept C204241405 @default.
• W2606536620 hasConcept C33923547 @default.
• W2606536620 hasConcept C41008148 @default.
• W2606536620 hasConcept C55493867 @default.
• W2606536620 hasConcept C74193536 @default.
• W2606536620 hasConceptScore W2606536620C104317684 @default.
• W2606536620 hasConceptScore W2606536620C105795698 @default.
• W2606536620 hasConceptScore W2606536620C118615104 @default.
• W2606536620 hasConceptScore W2606536620C122280245 @default.
• W2606536620 hasConceptScore W2606536620C12267149 @default.
• W2606536620 hasConceptScore W2606536620C153180895 @default.
• W2606536620 hasConceptScore W2606536620C153874254 @default.
• W2606536620 hasConceptScore W2606536620C154945302 @default.
• W2606536620 hasConceptScore W2606536620C182335926 @default.
• W2606536620 hasConceptScore W2606536620C185592680 @default.
• W2606536620 hasConceptScore W2606536620C204241405 @default.
• W2606536620 hasConceptScore W2606536620C33923547 @default.
• W2606536620 hasConceptScore W2606536620C41008148 @default.
• W2606536620 hasConceptScore W2606536620C55493867 @default.
• W2606536620 hasConceptScore W2606536620C74193536 @default.
• W2606536620 hasLocation W26065366201 @default.
• W2606536620 hasOpenAccess W2606536620 @default.
• W2606536620 hasPrimaryLocation W26065366201 @default.
• W2606536620 hasRelatedWork W1523385540 @default.
• W2606536620 hasRelatedWork W1965913815 @default.
• W2606536620 hasRelatedWork W2003271048 @default.
• W2606536620 hasRelatedWork W2025341678 @default.
• W2606536620 hasRelatedWork W2042683698 @default.
• W2606536620 hasRelatedWork W2047631354 @default.
• W2606536620 hasRelatedWork W2052964740 @default.
• W2606536620 hasRelatedWork W205396393 @default.
• W2606536620 hasRelatedWork W207564360 @default.
• W2606536620 hasRelatedWork W2100235303 @default.
• W2606536620 hasRelatedWork W2130055251 @default.
• W2606536620 hasRelatedWork W2294211726 @default.
• W2606536620 hasRelatedWork W2359473794 @default.
• W2606536620 hasRelatedWork W2747682498 @default.
• W2606536620 hasRelatedWork W2893104835 @default.
• W2606536620 hasRelatedWork W2898938806 @default.
• W2606536620 hasRelatedWork W2950749759 @default.
• W2606536620 hasRelatedWork W2961137952 @default.
• W2606536620 hasRelatedWork W3033253825 @default.
• W2606536620 hasRelatedWork W2181386156 @default.
• W2606536620 isParatext "false" @default.
• W2606536620 isRetracted "false" @default.
• W2606536620 magId "2606536620" @default.

• next