Matches in SemOpenAlex for { <https://semopenalex.org/work/W1995773010> ?p ?o ?g. }
- W1995773010 endingPage "389" @default.
- W1995773010 startingPage "377" @default.
- W1995773010 abstract "Multivariate images are very large data structures and any type of regression for their analysis is very computer-intensive. Kernel-based partial least squares (PLS) regression, presented in an earlier paper, makes the calculation phase more rapid and less demanding in computer memory. The present paper is a direct continuation of the first paper. In this study the kernel PLS algorithm is extended to include cross-validation for determination of the optimal model dimensionality. To show the applicability of the kernel algorithm, two examples from multivariate image analysis are used. The first example is an image from an airborne scanner of size 9 × 512 × 512. It consists of nine images which are regressed against a constructed dependent image to test the accuracy of the kernel algorithm when used on large data structures. The second example is a satellite image of size 7 × 512 × 512. Several different regression models are presented together with a comparison of their predictive capabilities. The regression models are also used as examples for showing the use of cross-validation." @default.
- W1995773010 created "2016-06-24" @default.
- W1995773010 creator A5001655358 @default.
- W1995773010 creator A5019110925 @default.
- W1995773010 creator A5066323251 @default.
- W1995773010 date "1994-11-01" @default.
- W1995773010 modified "2023-10-03" @default.
- W1995773010 title "Kernel-based PLS regression; Cross-validation and applications to spectral data" @default.
- W1995773010 cites W1542125476 @default.
- W1995773010 cites W1966089218 @default.
- W1995773010 cites W1974221753 @default.
- W1995773010 cites W1976251851 @default.
- W1995773010 cites W1979612645 @default.
- W1995773010 cites W1981730794 @default.
- W1995773010 cites W2005051528 @default.
- W1995773010 cites W2006532654 @default.
- W1995773010 cites W2015596806 @default.
- W1995773010 cites W2018982554 @default.
- W1995773010 cites W2022728259 @default.
- W1995773010 cites W2027225917 @default.
- W1995773010 cites W2046218673 @default.
- W1995773010 cites W2048522468 @default.
- W1995773010 cites W2052667128 @default.
- W1995773010 cites W2066090712 @default.
- W1995773010 cites W2079775628 @default.
- W1995773010 cites W2082984896 @default.
- W1995773010 cites W2087883561 @default.
- W1995773010 cites W2088791192 @default.
- W1995773010 cites W2096043274 @default.
- W1995773010 cites W2152176378 @default.
- W1995773010 cites W2158863190 @default.
- W1995773010 cites W2160333357 @default.
- W1995773010 cites W2166446427 @default.
- W1995773010 cites W2169016202 @default.
- W1995773010 cites W24821413 @default.
- W1995773010 cites W4234698323 @default.
- W1995773010 doi "https://doi.org/10.1002/cem.1180080604" @default.
- W1995773010 hasPublicationYear "1994" @default.
- W1995773010 type Work @default.
- W1995773010 sameAs 1995773010 @default.
- W1995773010 citedByCount "27" @default.
- W1995773010 countsByYear W19957730102012 @default.
- W1995773010 countsByYear W19957730102013 @default.
- W1995773010 countsByYear W19957730102014 @default.
- W1995773010 countsByYear W19957730102015 @default.
- W1995773010 countsByYear W19957730102016 @default.
- W1995773010 countsByYear W19957730102019 @default.
- W1995773010 countsByYear W19957730102021 @default.
- W1995773010 crossrefType "journal-article" @default.
- W1995773010 hasAuthorship W1995773010A5001655358 @default.
- W1995773010 hasAuthorship W1995773010A5019110925 @default.
- W1995773010 hasAuthorship W1995773010A5066323251 @default.
- W1995773010 hasConcept C105795698 @default.
- W1995773010 hasConcept C111030470 @default.
- W1995773010 hasConcept C11413529 @default.
- W1995773010 hasConcept C114614502 @default.
- W1995773010 hasConcept C119857082 @default.
- W1995773010 hasConcept C122280245 @default.
- W1995773010 hasConcept C12267149 @default.
- W1995773010 hasConcept C152877465 @default.
- W1995773010 hasConcept C153180895 @default.
- W1995773010 hasConcept C154945302 @default.
- W1995773010 hasConcept C161584116 @default.
- W1995773010 hasConcept C200695384 @default.
- W1995773010 hasConcept C22354355 @default.
- W1995773010 hasConcept C27181475 @default.
- W1995773010 hasConcept C33923547 @default.
- W1995773010 hasConcept C41008148 @default.
- W1995773010 hasConcept C74193536 @default.
- W1995773010 hasConcept C83546350 @default.
- W1995773010 hasConceptScore W1995773010C105795698 @default.
- W1995773010 hasConceptScore W1995773010C111030470 @default.
- W1995773010 hasConceptScore W1995773010C11413529 @default.
- W1995773010 hasConceptScore W1995773010C114614502 @default.
- W1995773010 hasConceptScore W1995773010C119857082 @default.
- W1995773010 hasConceptScore W1995773010C122280245 @default.
- W1995773010 hasConceptScore W1995773010C12267149 @default.
- W1995773010 hasConceptScore W1995773010C152877465 @default.
- W1995773010 hasConceptScore W1995773010C153180895 @default.
- W1995773010 hasConceptScore W1995773010C154945302 @default.
- W1995773010 hasConceptScore W1995773010C161584116 @default.
- W1995773010 hasConceptScore W1995773010C200695384 @default.
- W1995773010 hasConceptScore W1995773010C22354355 @default.
- W1995773010 hasConceptScore W1995773010C27181475 @default.
- W1995773010 hasConceptScore W1995773010C33923547 @default.
- W1995773010 hasConceptScore W1995773010C41008148 @default.
- W1995773010 hasConceptScore W1995773010C74193536 @default.
- W1995773010 hasConceptScore W1995773010C83546350 @default.
- W1995773010 hasIssue "6" @default.
- W1995773010 hasLocation W19957730101 @default.
- W1995773010 hasOpenAccess W1995773010 @default.
- W1995773010 hasPrimaryLocation W19957730101 @default.
- W1995773010 hasRelatedWork W1965631957 @default.
- W1995773010 hasRelatedWork W2044772846 @default.
- W1995773010 hasRelatedWork W2097028249 @default.
- W1995773010 hasRelatedWork W2102031562 @default.
- W1995773010 hasRelatedWork W2110459882 @default.
- W1995773010 hasRelatedWork W2123450736 @default.