Matches in SemOpenAlex for { <https://semopenalex.org/work/W2072620639> ?p ?o ?g. }
Showing items 1 to 54 of
54
with 100 items per page.
- W2072620639 abstract "Independent Factor Analysis (IFA) is used to recover latent components (or sources) from their linear observed mixtures within an un- supervised learning framework. Both the mixing process and the source densities are learned from the observed data. The sources are assumed to be mutually independent and distributed according to a mixture of Gaussians. This paper investigates the possibility of incorporating partial knowledge on the cluster belonging of some samples to estimate the IFA model. Semi-supervised and partially supervised learning cases can thus be handled. Experimental results demonstrate the ability of this approach to enhance estimation accuracy and remove indeterminacy commonly en- countered in unsupervised IFA such as the permutation of the sources." @default.
- W2072620639 created "2016-06-24" @default.
- W2072620639 creator A5011761326 @default.
- W2072620639 creator A5029516915 @default.
- W2072620639 creator A5052312255 @default.
- W2072620639 creator A5087274722 @default.
- W2072620639 date "2009-04-22" @default.
- W2072620639 modified "2023-10-18" @default.
- W2072620639 title "Partially-supervised learning in Independent Factor Analysis" @default.
- W2072620639 cites W1489793438 @default.
- W2072620639 cites W2101303708 @default.
- W2072620639 cites W2108384452 @default.
- W2072620639 cites W2128314196 @default.
- W2072620639 cites W2133069808 @default.
- W2072620639 cites W2137969290 @default.
- W2072620639 cites W2797148637 @default.
- W2072620639 hasPublicationYear "2009" @default.
- W2072620639 type Work @default.
- W2072620639 sameAs 2072620639 @default.
- W2072620639 citedByCount "1" @default.
- W2072620639 crossrefType "proceedings-article" @default.
- W2072620639 hasAuthorship W2072620639A5011761326 @default.
- W2072620639 hasAuthorship W2072620639A5029516915 @default.
- W2072620639 hasAuthorship W2072620639A5052312255 @default.
- W2072620639 hasAuthorship W2072620639A5087274722 @default.
- W2072620639 hasBestOaLocation W20726206391 @default.
- W2072620639 hasConcept C119857082 @default.
- W2072620639 hasConcept C154945302 @default.
- W2072620639 hasConcept C199360897 @default.
- W2072620639 hasConcept C2781039887 @default.
- W2072620639 hasConcept C41008148 @default.
- W2072620639 hasConceptScore W2072620639C119857082 @default.
- W2072620639 hasConceptScore W2072620639C154945302 @default.
- W2072620639 hasConceptScore W2072620639C199360897 @default.
- W2072620639 hasConceptScore W2072620639C2781039887 @default.
- W2072620639 hasConceptScore W2072620639C41008148 @default.
- W2072620639 hasLocation W20726206391 @default.
- W2072620639 hasLocation W20726206392 @default.
- W2072620639 hasOpenAccess W2072620639 @default.
- W2072620639 hasPrimaryLocation W20726206391 @default.
- W2072620639 hasRelatedWork W2961085424 @default.
- W2072620639 hasRelatedWork W3046775127 @default.
- W2072620639 hasRelatedWork W3107602296 @default.
- W2072620639 hasRelatedWork W3170094116 @default.
- W2072620639 hasRelatedWork W3209574120 @default.
- W2072620639 hasRelatedWork W4210805261 @default.
- W2072620639 hasRelatedWork W4306674287 @default.
- W2072620639 hasRelatedWork W4312192474 @default.
- W2072620639 hasRelatedWork W4386462264 @default.
- W2072620639 hasRelatedWork W4387297750 @default.
- W2072620639 isParatext "false" @default.
- W2072620639 isRetracted "false" @default.
- W2072620639 magId "2072620639" @default.
- W2072620639 workType "article" @default.