Matches in SemOpenAlex for { <https://semopenalex.org/work/W1607408214> ?p ?o ?g. }
- W1607408214 endingPage "678" @default.
- W1607408214 startingPage "673" @default.
- W1607408214 abstract "Spectral feature selection identifies relevant features by measuring their capability of preserving sample similarity. It provides a powerful framework for both supervised and unsupervised feature selection, and has been proven to be effective in many real-world applications. One common drawback associated with most existing spectral feature selection algorithms is that they evaluate features individually and cannot identify redundant features. Since redundant features can have significant adverse effect on learning performance, it is necessary to address this limitation for spectral feature selection. To this end, we propose a novel spectral feature selection algorithm to handle feature redundancy, adopting an embedded model. The algorithm is derived from a formulation based on a sparse multi-output regression with a L2,1-norm constraint. We conduct theoretical analysis on the properties of its optimal solutions, paving the way for designing an efficient path-following solver. Extensive experiments show that the proposed algorithm can do well in both selecting relevant features and removing redundancy." @default.
- W1607408214 created "2016-06-24" @default.
- W1607408214 creator A5002117195 @default.
- W1607408214 creator A5013881064 @default.
- W1607408214 creator A5073216396 @default.
- W1607408214 date "2010-07-03" @default.
- W1607408214 modified "2023-10-11" @default.
- W1607408214 title "Efficient Spectral Feature Selection with Minimum Redundancy" @default.
- W1607408214 cites W1480376833 @default.
- W1607408214 cites W1495061682 @default.
- W1607408214 cites W1500895378 @default.
- W1607408214 cites W1520252399 @default.
- W1607408214 cites W1570713908 @default.
- W1607408214 cites W1596717185 @default.
- W1607408214 cites W1871180460 @default.
- W1607408214 cites W1999085008 @default.
- W1607408214 cites W2050559027 @default.
- W1607408214 cites W2065180801 @default.
- W1607408214 cites W2101267652 @default.
- W1607408214 cites W2112219590 @default.
- W1607408214 cites W2115777736 @default.
- W1607408214 cites W2119479037 @default.
- W1607408214 cites W2121007818 @default.
- W1607408214 cites W2124225314 @default.
- W1607408214 cites W2131987814 @default.
- W1607408214 cites W2132914434 @default.
- W1607408214 cites W2149620660 @default.
- W1607408214 cites W2150079332 @default.
- W1607408214 cites W2156571267 @default.
- W1607408214 cites W2158497838 @default.
- W1607408214 cites W2158933803 @default.
- W1607408214 cites W2166471851 @default.
- W1607408214 cites W2296319761 @default.
- W1607408214 cites W2799061466 @default.
- W1607408214 doi "https://doi.org/10.1609/aaai.v24i1.7671" @default.
- W1607408214 hasPublicationYear "2010" @default.
- W1607408214 type Work @default.
- W1607408214 sameAs 1607408214 @default.
- W1607408214 citedByCount "131" @default.
- W1607408214 countsByYear W16074082142012 @default.
- W1607408214 countsByYear W16074082142013 @default.
- W1607408214 countsByYear W16074082142014 @default.
- W1607408214 countsByYear W16074082142015 @default.
- W1607408214 countsByYear W16074082142016 @default.
- W1607408214 countsByYear W16074082142017 @default.
- W1607408214 countsByYear W16074082142018 @default.
- W1607408214 countsByYear W16074082142019 @default.
- W1607408214 countsByYear W16074082142020 @default.
- W1607408214 countsByYear W16074082142021 @default.
- W1607408214 countsByYear W16074082142022 @default.
- W1607408214 countsByYear W16074082142023 @default.
- W1607408214 crossrefType "journal-article" @default.
- W1607408214 hasAuthorship W1607408214A5002117195 @default.
- W1607408214 hasAuthorship W1607408214A5013881064 @default.
- W1607408214 hasAuthorship W1607408214A5073216396 @default.
- W1607408214 hasBestOaLocation W16074082141 @default.
- W1607408214 hasConcept C111919701 @default.
- W1607408214 hasConcept C11413529 @default.
- W1607408214 hasConcept C119857082 @default.
- W1607408214 hasConcept C124101348 @default.
- W1607408214 hasConcept C138885662 @default.
- W1607408214 hasConcept C148483581 @default.
- W1607408214 hasConcept C152124472 @default.
- W1607408214 hasConcept C153180895 @default.
- W1607408214 hasConcept C154945302 @default.
- W1607408214 hasConcept C16811321 @default.
- W1607408214 hasConcept C2776401178 @default.
- W1607408214 hasConcept C41008148 @default.
- W1607408214 hasConcept C41895202 @default.
- W1607408214 hasConceptScore W1607408214C111919701 @default.
- W1607408214 hasConceptScore W1607408214C11413529 @default.
- W1607408214 hasConceptScore W1607408214C119857082 @default.
- W1607408214 hasConceptScore W1607408214C124101348 @default.
- W1607408214 hasConceptScore W1607408214C138885662 @default.
- W1607408214 hasConceptScore W1607408214C148483581 @default.
- W1607408214 hasConceptScore W1607408214C152124472 @default.
- W1607408214 hasConceptScore W1607408214C153180895 @default.
- W1607408214 hasConceptScore W1607408214C154945302 @default.
- W1607408214 hasConceptScore W1607408214C16811321 @default.
- W1607408214 hasConceptScore W1607408214C2776401178 @default.
- W1607408214 hasConceptScore W1607408214C41008148 @default.
- W1607408214 hasConceptScore W1607408214C41895202 @default.
- W1607408214 hasIssue "1" @default.
- W1607408214 hasLocation W16074082141 @default.
- W1607408214 hasLocation W16074082142 @default.
- W1607408214 hasOpenAccess W1607408214 @default.
- W1607408214 hasPrimaryLocation W16074082141 @default.
- W1607408214 hasRelatedWork W2159220931 @default.
- W1607408214 hasRelatedWork W2163070219 @default.
- W1607408214 hasRelatedWork W2286904880 @default.
- W1607408214 hasRelatedWork W2352657000 @default.
- W1607408214 hasRelatedWork W2374344280 @default.
- W1607408214 hasRelatedWork W2392236103 @default.
- W1607408214 hasRelatedWork W3015830444 @default.
- W1607408214 hasRelatedWork W4293660994 @default.
- W1607408214 hasRelatedWork W4312247183 @default.
- W1607408214 hasRelatedWork W4319922723 @default.
- W1607408214 hasVolume "24" @default.