Matches in SemOpenAlex for { <https://semopenalex.org/work/W2783129721> ?p ?o ?g. }
Showing items 1 to 89 of
89
with 100 items per page.
- W2783129721 abstract "The lack of labels and the poor quality of data present a common challenge in many data mining and machine learning problems. The model performance might be limited if only a few labeled samples are available for training. Moreover, the data may be noisy in reality, which disturbs the data distribution and further hinders the learning performance. These problems become even more critical in multi-label classification, which has an intricate label space and usually requires clean data for training. In this paper, we aim to tackle the above problems by learning effective feature representations for semi-supervised multi-label classification. We propose a novel approach named Adaptive Low-rank Semi-supervised learning for Multi-label classification (ALSM). It learns an intermediate feature space for both labeled and unlabeled training samples via low-rank matrix recovery, and employs an adaptive semi-supervised learning strategy to train a multi-label classifier. We solve the problem by devising an optimization algorithm based on the Alternating Direction Method of Multipliers (ADMM). Our approach can be applied to both transductive and inductive semi-supervised multi-label classification problems. Experiments on five benchmark multi-label datasets show that our approach outperforms the representative multi-label classification methods in most cases." @default.
- W2783129721 created "2018-01-26" @default.
- W2783129721 creator A5005819096 @default.
- W2783129721 creator A5026512168 @default.
- W2783129721 date "2017-12-01" @default.
- W2783129721 modified "2023-09-26" @default.
- W2783129721 title "Robust multi-label semi-supervised classification" @default.
- W2783129721 cites W1513235864 @default.
- W2783129721 cites W1514928307 @default.
- W2783129721 cites W1566135517 @default.
- W2783129721 cites W1894767581 @default.
- W2783129721 cites W1969954882 @default.
- W2783129721 cites W1980061733 @default.
- W2783129721 cites W1997201895 @default.
- W2783129721 cites W2007588387 @default.
- W2783129721 cites W2019899889 @default.
- W2783129721 cites W2044544672 @default.
- W2783129721 cites W2083177649 @default.
- W2783129721 cites W2097622337 @default.
- W2783129721 cites W2103972604 @default.
- W2783129721 cites W2114315281 @default.
- W2783129721 cites W2122712590 @default.
- W2783129721 cites W2145962650 @default.
- W2783129721 cites W2166338096 @default.
- W2783129721 cites W2241072627 @default.
- W2783129721 cites W2273827415 @default.
- W2783129721 cites W2279365017 @default.
- W2783129721 cites W2336020590 @default.
- W2783129721 cites W2395434972 @default.
- W2783129721 cites W2584571478 @default.
- W2783129721 cites W2743493499 @default.
- W2783129721 cites W2745150923 @default.
- W2783129721 cites W813887593 @default.
- W2783129721 doi "https://doi.org/10.1109/bigdata.2017.8257908" @default.
- W2783129721 hasPublicationYear "2017" @default.
- W2783129721 type Work @default.
- W2783129721 sameAs 2783129721 @default.
- W2783129721 citedByCount "3" @default.
- W2783129721 countsByYear W27831297212018 @default.
- W2783129721 countsByYear W27831297212020 @default.
- W2783129721 countsByYear W27831297212021 @default.
- W2783129721 crossrefType "proceedings-article" @default.
- W2783129721 hasAuthorship W2783129721A5005819096 @default.
- W2783129721 hasAuthorship W2783129721A5026512168 @default.
- W2783129721 hasConcept C119857082 @default.
- W2783129721 hasConcept C13280743 @default.
- W2783129721 hasConcept C136389625 @default.
- W2783129721 hasConcept C139532973 @default.
- W2783129721 hasConcept C153180895 @default.
- W2783129721 hasConcept C154945302 @default.
- W2783129721 hasConcept C185798385 @default.
- W2783129721 hasConcept C205649164 @default.
- W2783129721 hasConcept C2776145971 @default.
- W2783129721 hasConcept C2776482837 @default.
- W2783129721 hasConcept C41008148 @default.
- W2783129721 hasConcept C50644808 @default.
- W2783129721 hasConcept C58973888 @default.
- W2783129721 hasConcept C95623464 @default.
- W2783129721 hasConceptScore W2783129721C119857082 @default.
- W2783129721 hasConceptScore W2783129721C13280743 @default.
- W2783129721 hasConceptScore W2783129721C136389625 @default.
- W2783129721 hasConceptScore W2783129721C139532973 @default.
- W2783129721 hasConceptScore W2783129721C153180895 @default.
- W2783129721 hasConceptScore W2783129721C154945302 @default.
- W2783129721 hasConceptScore W2783129721C185798385 @default.
- W2783129721 hasConceptScore W2783129721C205649164 @default.
- W2783129721 hasConceptScore W2783129721C2776145971 @default.
- W2783129721 hasConceptScore W2783129721C2776482837 @default.
- W2783129721 hasConceptScore W2783129721C41008148 @default.
- W2783129721 hasConceptScore W2783129721C50644808 @default.
- W2783129721 hasConceptScore W2783129721C58973888 @default.
- W2783129721 hasConceptScore W2783129721C95623464 @default.
- W2783129721 hasLocation W27831297211 @default.
- W2783129721 hasOpenAccess W2783129721 @default.
- W2783129721 hasPrimaryLocation W27831297211 @default.
- W2783129721 hasRelatedWork W1988412055 @default.
- W2783129721 hasRelatedWork W2134684456 @default.
- W2783129721 hasRelatedWork W2151561819 @default.
- W2783129721 hasRelatedWork W2160952319 @default.
- W2783129721 hasRelatedWork W2538661024 @default.
- W2783129721 hasRelatedWork W2542037019 @default.
- W2783129721 hasRelatedWork W2743493499 @default.
- W2783129721 hasRelatedWork W2783129721 @default.
- W2783129721 hasRelatedWork W3162567751 @default.
- W2783129721 hasRelatedWork W4294974824 @default.
- W2783129721 isParatext "false" @default.
- W2783129721 isRetracted "false" @default.
- W2783129721 magId "2783129721" @default.
- W2783129721 workType "article" @default.