Matches in SemOpenAlex for { <https://semopenalex.org/work/W2522570892> ?p ?o ?g. }
Showing items 1 to 95 of
95
with 100 items per page.
- W2522570892 abstract "The ubiquitous digit devices, sensors and social networks bring tremendous high-dimensional data. The high-dimensionality leads to high time complexity, large storage burden, and degradation of the generalization ability. Subspace learning is one of the most effective ways to eliminate the curse of dimensionality by projecting the data to a low-dimensional feature subspace. In this paper, we proposed a novel unsupervised feature dimension reduction method via analysis dictionary learning. By learning an analysis dictionary, we project a sample to a low-dimensional space and the feature dimension is the number of atoms in the dictionary. The coding coefficient vector is used as the low-dimensional representation of data because it reflects the distribution on the synthesis dictionary atoms. Manifold regularization is imposed on the low-dimensional representation of data to keep the locality of the original feature space. Experiments on four datasets show that the proposed unsupervised dimension reduction model outperforms the state-of-the-art methods." @default.
- W2522570892 created "2016-09-30" @default.
- W2522570892 creator A5002582422 @default.
- W2522570892 creator A5006952581 @default.
- W2522570892 creator A5020095072 @default.
- W2522570892 creator A5056686459 @default.
- W2522570892 date "2016-01-01" @default.
- W2522570892 modified "2023-09-22" @default.
- W2522570892 title "Unsupervised Subspace Learning via Analysis Dictionary Learning" @default.
- W2522570892 cites W1672347394 @default.
- W2522570892 cites W1948068958 @default.
- W2522570892 cites W1997348485 @default.
- W2522570892 cites W2070127246 @default.
- W2522570892 cites W2097308346 @default.
- W2522570892 cites W2098693229 @default.
- W2522570892 cites W2105437767 @default.
- W2522570892 cites W2110662122 @default.
- W2522570892 cites W2143115034 @default.
- W2522570892 cites W2145962650 @default.
- W2522570892 cites W4292363360 @default.
- W2522570892 doi "https://doi.org/10.1007/978-3-319-46654-5_61" @default.
- W2522570892 hasPublicationYear "2016" @default.
- W2522570892 type Work @default.
- W2522570892 sameAs 2522570892 @default.
- W2522570892 citedByCount "0" @default.
- W2522570892 crossrefType "book-chapter" @default.
- W2522570892 hasAuthorship W2522570892A5002582422 @default.
- W2522570892 hasAuthorship W2522570892A5006952581 @default.
- W2522570892 hasAuthorship W2522570892A5020095072 @default.
- W2522570892 hasAuthorship W2522570892A5056686459 @default.
- W2522570892 hasConcept C111030470 @default.
- W2522570892 hasConcept C138885662 @default.
- W2522570892 hasConcept C151876577 @default.
- W2522570892 hasConcept C153180895 @default.
- W2522570892 hasConcept C154945302 @default.
- W2522570892 hasConcept C184509293 @default.
- W2522570892 hasConcept C202444582 @default.
- W2522570892 hasConcept C2779808786 @default.
- W2522570892 hasConcept C30732413 @default.
- W2522570892 hasConcept C32834561 @default.
- W2522570892 hasConcept C33676613 @default.
- W2522570892 hasConcept C33923547 @default.
- W2522570892 hasConcept C41008148 @default.
- W2522570892 hasConcept C41895202 @default.
- W2522570892 hasConcept C59404180 @default.
- W2522570892 hasConcept C70518039 @default.
- W2522570892 hasConcept C73555534 @default.
- W2522570892 hasConcept C8038995 @default.
- W2522570892 hasConcept C83665646 @default.
- W2522570892 hasConceptScore W2522570892C111030470 @default.
- W2522570892 hasConceptScore W2522570892C138885662 @default.
- W2522570892 hasConceptScore W2522570892C151876577 @default.
- W2522570892 hasConceptScore W2522570892C153180895 @default.
- W2522570892 hasConceptScore W2522570892C154945302 @default.
- W2522570892 hasConceptScore W2522570892C184509293 @default.
- W2522570892 hasConceptScore W2522570892C202444582 @default.
- W2522570892 hasConceptScore W2522570892C2779808786 @default.
- W2522570892 hasConceptScore W2522570892C30732413 @default.
- W2522570892 hasConceptScore W2522570892C32834561 @default.
- W2522570892 hasConceptScore W2522570892C33676613 @default.
- W2522570892 hasConceptScore W2522570892C33923547 @default.
- W2522570892 hasConceptScore W2522570892C41008148 @default.
- W2522570892 hasConceptScore W2522570892C41895202 @default.
- W2522570892 hasConceptScore W2522570892C59404180 @default.
- W2522570892 hasConceptScore W2522570892C70518039 @default.
- W2522570892 hasConceptScore W2522570892C73555534 @default.
- W2522570892 hasConceptScore W2522570892C8038995 @default.
- W2522570892 hasConceptScore W2522570892C83665646 @default.
- W2522570892 hasLocation W25225708921 @default.
- W2522570892 hasOpenAccess W2522570892 @default.
- W2522570892 hasPrimaryLocation W25225708921 @default.
- W2522570892 hasRelatedWork W2012982311 @default.
- W2522570892 hasRelatedWork W2092668337 @default.
- W2522570892 hasRelatedWork W2295388844 @default.
- W2522570892 hasRelatedWork W2344457357 @default.
- W2522570892 hasRelatedWork W2390941688 @default.
- W2522570892 hasRelatedWork W2506832951 @default.
- W2522570892 hasRelatedWork W2508240147 @default.
- W2522570892 hasRelatedWork W2521448411 @default.
- W2522570892 hasRelatedWork W2592516688 @default.
- W2522570892 hasRelatedWork W2770749234 @default.
- W2522570892 hasRelatedWork W2912383323 @default.
- W2522570892 hasRelatedWork W2918762554 @default.
- W2522570892 hasRelatedWork W2925005147 @default.
- W2522570892 hasRelatedWork W2963069872 @default.
- W2522570892 hasRelatedWork W3041891788 @default.
- W2522570892 hasRelatedWork W3088801022 @default.
- W2522570892 hasRelatedWork W3112915286 @default.
- W2522570892 hasRelatedWork W3137891897 @default.
- W2522570892 hasRelatedWork W3153894981 @default.
- W2522570892 hasRelatedWork W3163753946 @default.
- W2522570892 isParatext "false" @default.
- W2522570892 isRetracted "false" @default.
- W2522570892 magId "2522570892" @default.
- W2522570892 workType "book-chapter" @default.