Matches in SemOpenAlex for { <https://semopenalex.org/work/W1987015131> ?p ?o ?g. }
Showing items 1 to 94 of
94
with 100 items per page.
- W1987015131 abstract "Traditional data mining techniques are designed to model a single type of heterogeneity, such as multi-task learning for modeling task heterogeneity, multi-view learning for modeling view heterogeneity, etc. Recently, a variety of real applications emerged, which exhibit dual heterogeneity, namely both task heterogeneity and view heterogeneity. Examples include insider threat detection across multiple organizations, web image classification in different domains, etc. Existing methods for addressing such problems typically assume that multiple tasks are equally related and multiple views are equally consistent, which limits their application in complex settings with varying task relatedness and view consistency. In this paper, we advance state-of-the-art techniques by adaptively modeling task relatedness and view consistency via a nonparametric Bayes model: we model task relatedness using normal penalty with sparse covariances, and view consistency using matrix Dirichlet process. Based on this model, we propose the NOBLE algorithm using an efficient Gibbs sampler. Experimental results on multiple real data sets demonstrate the effectiveness of the proposed algorithm." @default.
- W1987015131 created "2016-06-24" @default.
- W1987015131 creator A5073158087 @default.
- W1987015131 creator A5082599714 @default.
- W1987015131 date "2014-08-24" @default.
- W1987015131 modified "2023-09-26" @default.
- W1987015131 title "Learning with dual heterogeneity" @default.
- W1987015131 cites W1967687583 @default.
- W1987015131 cites W1986744686 @default.
- W1987015131 cites W2018096278 @default.
- W1987015131 cites W2037603696 @default.
- W1987015131 cites W2038531878 @default.
- W1987015131 cites W2040649920 @default.
- W1987015131 cites W2048679005 @default.
- W1987015131 cites W2049310227 @default.
- W1987015131 cites W2049738627 @default.
- W1987015131 cites W2053483403 @default.
- W1987015131 cites W2069429561 @default.
- W1987015131 cites W2072942628 @default.
- W1987015131 cites W2078464983 @default.
- W1987015131 cites W2089484716 @default.
- W1987015131 cites W2107021927 @default.
- W1987015131 cites W2111583736 @default.
- W1987015131 cites W2119187866 @default.
- W1987015131 cites W2125027820 @default.
- W1987015131 cites W2130903752 @default.
- W1987015131 cites W2148522164 @default.
- W1987015131 cites W2165260680 @default.
- W1987015131 cites W2168975192 @default.
- W1987015131 cites W2405793168 @default.
- W1987015131 cites W3010453621 @default.
- W1987015131 doi "https://doi.org/10.1145/2623330.2623727" @default.
- W1987015131 hasPublicationYear "2014" @default.
- W1987015131 type Work @default.
- W1987015131 sameAs 1987015131 @default.
- W1987015131 citedByCount "12" @default.
- W1987015131 countsByYear W19870151312015 @default.
- W1987015131 countsByYear W19870151312016 @default.
- W1987015131 countsByYear W19870151312017 @default.
- W1987015131 countsByYear W19870151312018 @default.
- W1987015131 crossrefType "proceedings-article" @default.
- W1987015131 hasAuthorship W1987015131A5073158087 @default.
- W1987015131 hasAuthorship W1987015131A5082599714 @default.
- W1987015131 hasConcept C119857082 @default.
- W1987015131 hasConcept C124101348 @default.
- W1987015131 hasConcept C124952713 @default.
- W1987015131 hasConcept C136197465 @default.
- W1987015131 hasConcept C141318989 @default.
- W1987015131 hasConcept C142362112 @default.
- W1987015131 hasConcept C154945302 @default.
- W1987015131 hasConcept C162324750 @default.
- W1987015131 hasConcept C171686336 @default.
- W1987015131 hasConcept C187736073 @default.
- W1987015131 hasConcept C2776214188 @default.
- W1987015131 hasConcept C2776436953 @default.
- W1987015131 hasConcept C2780451532 @default.
- W1987015131 hasConcept C2780980858 @default.
- W1987015131 hasConcept C2781280628 @default.
- W1987015131 hasConcept C41008148 @default.
- W1987015131 hasConcept C500882744 @default.
- W1987015131 hasConceptScore W1987015131C119857082 @default.
- W1987015131 hasConceptScore W1987015131C124101348 @default.
- W1987015131 hasConceptScore W1987015131C124952713 @default.
- W1987015131 hasConceptScore W1987015131C136197465 @default.
- W1987015131 hasConceptScore W1987015131C141318989 @default.
- W1987015131 hasConceptScore W1987015131C142362112 @default.
- W1987015131 hasConceptScore W1987015131C154945302 @default.
- W1987015131 hasConceptScore W1987015131C162324750 @default.
- W1987015131 hasConceptScore W1987015131C171686336 @default.
- W1987015131 hasConceptScore W1987015131C187736073 @default.
- W1987015131 hasConceptScore W1987015131C2776214188 @default.
- W1987015131 hasConceptScore W1987015131C2776436953 @default.
- W1987015131 hasConceptScore W1987015131C2780451532 @default.
- W1987015131 hasConceptScore W1987015131C2780980858 @default.
- W1987015131 hasConceptScore W1987015131C2781280628 @default.
- W1987015131 hasConceptScore W1987015131C41008148 @default.
- W1987015131 hasConceptScore W1987015131C500882744 @default.
- W1987015131 hasLocation W19870151311 @default.
- W1987015131 hasOpenAccess W1987015131 @default.
- W1987015131 hasPrimaryLocation W19870151311 @default.
- W1987015131 hasRelatedWork W1481285460 @default.
- W1987015131 hasRelatedWork W1545357565 @default.
- W1987015131 hasRelatedWork W2183466550 @default.
- W1987015131 hasRelatedWork W2403843452 @default.
- W1987015131 hasRelatedWork W2996472503 @default.
- W1987015131 hasRelatedWork W2997946860 @default.
- W1987015131 hasRelatedWork W3028393285 @default.
- W1987015131 hasRelatedWork W3121849826 @default.
- W1987015131 hasRelatedWork W4281686909 @default.
- W1987015131 hasRelatedWork W4295885086 @default.
- W1987015131 isParatext "false" @default.
- W1987015131 isRetracted "false" @default.
- W1987015131 magId "1987015131" @default.
- W1987015131 workType "article" @default.