FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2076823625> ?p ?o ?g. }

• W2076823625 abstract "Multiple kernel learning (MKL) is demonstrated to be flexible and effective in depicting heterogeneous data sources since MKL can introduce multiple kernels rather than a single fixed kernel into applications. However, MKL would get a high time and space complexity in contrast to single kernel learning, which is not expected in real-world applications. Meanwhile, it is known that the kernel mapping ways of MKL generally have two forms including implicit kernel mapping and empirical kernel mapping (EKM), where the latter is less attracted. In this paper, we focus on the MKL with the EKM, and propose a reduced multiple empirical kernel learning machine named RMEKLM for short. To the best of our knowledge, it is the first to reduce both time and space complexity of the MKL with EKM. Different from the existing MKL, the proposed RMEKLM adopts the Gauss Elimination technique to extract a set of feature vectors, which is validated that doing so does not lose much information of the original feature space. Then RMEKLM adopts the extracted feature vectors to span a reduced orthonormal subspace of the feature space, which is visualized in terms of the geometry structure. It can be demonstrated that the spanned subspace is isomorphic to the original feature space, which means that the dot product of two vectors in the original feature space is equal to that of the two corresponding vectors in the generated orthonormal subspace. More importantly, the proposed RMEKLM brings a simpler computation and meanwhile needs a less storage space, especially in the processing of testing. Finally, the experimental results show that RMEKLM owns a much efficient and effective performance in terms of both complexity and classification. The contributions of this paper can be given as follows: (1) by mapping the input space into an orthonormal subspace, the geometry of the generated subspace is visualized; (2) this paper first reduces both the time and space complexity of the EKM-based MKL; (3) this paper adopts the Gauss Elimination, one of the on-the-shelf techniques, to generate a basis of the original feature space, which is stable and efficient." @default.
• W2076823625 created "2016-06-24" @default.
• W2076823625 creator A5007052738 @default.
• W2076823625 creator A5056053058 @default.
• W2076823625 creator A5075013431 @default.
• W2076823625 date "2014-07-29" @default.
• W2076823625 modified "2023-09-24" @default.
• W2076823625 title "Reduced multiple empirical kernel learning machine" @default.
• W2076823625 cites W1563088657 @default.
• W2076823625 cites W2001938796 @default.
• W2076823625 cites W2004729645 @default.
• W2076823625 cites W2006676204 @default.
• W2076823625 cites W2013795140 @default.
• W2076823625 cites W2016406192 @default.
• W2076823625 cites W2018762151 @default.
• W2076823625 cites W2031823405 @default.
• W2076823625 cites W2088032561 @default.
• W2076823625 cites W2100235303 @default.
• W2076823625 cites W2108995755 @default.
• W2076823625 cites W2112519824 @default.
• W2076823625 cites W2117991580 @default.
• W2076823625 cites W2122664326 @default.
• W2076823625 cites W2140389641 @default.
• W2076823625 cites W2144299089 @default.
• W2076823625 cites W2150772522 @default.
• W2076823625 cites W2162479195 @default.
• W2076823625 cites W2167416974 @default.
• W2076823625 cites W2610681481 @default.
• W2076823625 doi "https://doi.org/10.1007/s11571-014-9304-2" @default.
• W2076823625 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4454124" @default.
• W2076823625 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26052363" @default.
• W2076823625 hasPublicationYear "2014" @default.
• W2076823625 type Work @default.
• W2076823625 sameAs 2076823625 @default.
• W2076823625 citedByCount "0" @default.
• W2076823625 crossrefType "journal-article" @default.
• W2076823625 hasAuthorship W2076823625A5007052738 @default.
• W2076823625 hasAuthorship W2076823625A5056053058 @default.
• W2076823625 hasAuthorship W2076823625A5075013431 @default.
• W2076823625 hasBestOaLocation W20768236252 @default.
• W2076823625 hasConcept C118615104 @default.
• W2076823625 hasConcept C121332964 @default.
• W2076823625 hasConcept C122280245 @default.
• W2076823625 hasConcept C12267149 @default.
• W2076823625 hasConcept C134517425 @default.
• W2076823625 hasConcept C138885662 @default.
• W2076823625 hasConcept C140417398 @default.
• W2076823625 hasConcept C153180895 @default.
• W2076823625 hasConcept C154945302 @default.
• W2076823625 hasConcept C2776401178 @default.
• W2076823625 hasConcept C2776879701 @default.
• W2076823625 hasConcept C32834561 @default.
• W2076823625 hasConcept C33923547 @default.
• W2076823625 hasConcept C41008148 @default.
• W2076823625 hasConcept C41895202 @default.
• W2076823625 hasConcept C5806529 @default.
• W2076823625 hasConcept C62520636 @default.
• W2076823625 hasConcept C74193536 @default.
• W2076823625 hasConcept C83665646 @default.
• W2076823625 hasConceptScore W2076823625C118615104 @default.
• W2076823625 hasConceptScore W2076823625C121332964 @default.
• W2076823625 hasConceptScore W2076823625C122280245 @default.
• W2076823625 hasConceptScore W2076823625C12267149 @default.
• W2076823625 hasConceptScore W2076823625C134517425 @default.
• W2076823625 hasConceptScore W2076823625C138885662 @default.
• W2076823625 hasConceptScore W2076823625C140417398 @default.
• W2076823625 hasConceptScore W2076823625C153180895 @default.
• W2076823625 hasConceptScore W2076823625C154945302 @default.
• W2076823625 hasConceptScore W2076823625C2776401178 @default.
• W2076823625 hasConceptScore W2076823625C2776879701 @default.
• W2076823625 hasConceptScore W2076823625C32834561 @default.
• W2076823625 hasConceptScore W2076823625C33923547 @default.
• W2076823625 hasConceptScore W2076823625C41008148 @default.
• W2076823625 hasConceptScore W2076823625C41895202 @default.
• W2076823625 hasConceptScore W2076823625C5806529 @default.
• W2076823625 hasConceptScore W2076823625C62520636 @default.
• W2076823625 hasConceptScore W2076823625C74193536 @default.
• W2076823625 hasConceptScore W2076823625C83665646 @default.
• W2076823625 hasLocation W20768236251 @default.
• W2076823625 hasLocation W20768236252 @default.
• W2076823625 hasLocation W20768236253 @default.
• W2076823625 hasLocation W20768236254 @default.
• W2076823625 hasOpenAccess W2076823625 @default.
• W2076823625 hasPrimaryLocation W20768236251 @default.
• W2076823625 hasRelatedWork W158471633 @default.
• W2076823625 hasRelatedWork W1831484344 @default.
• W2076823625 hasRelatedWork W1991395427 @default.
• W2076823625 hasRelatedWork W1993493674 @default.
• W2076823625 hasRelatedWork W2057923398 @default.
• W2076823625 hasRelatedWork W2098957565 @default.
• W2076823625 hasRelatedWork W2121506664 @default.
• W2076823625 hasRelatedWork W2158413235 @default.
• W2076823625 hasRelatedWork W2490995046 @default.
• W2076823625 hasRelatedWork W2535206775 @default.
• W2076823625 hasRelatedWork W2586501666 @default.
• W2076823625 hasRelatedWork W2807855560 @default.
• W2076823625 hasRelatedWork W2989832332 @default.
• W2076823625 hasRelatedWork W3025752507 @default.
• W2076823625 hasRelatedWork W3088590578 @default.
• W2076823625 hasRelatedWork W3100948281 @default.

• next