Multi-View Multi-Label Learning With Sparse Feature Selection for Image Annotation

In image analysis, image samples are always represented by multiple view features and associated with multiple class labels for better interpretation. However, multiple view data may include noisy, irrelevant and redundant features, while multiple class labels can be noisy and incomplete. Due to the...

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Vydané v:	IEEE transactions on multimedia Ročník 22; číslo 11; s. 2844 - 2857
Hlavní autori:	Zhang, Yongshan, Wu, Jia, Cai, Zhihua, Yu, Philip S.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.11.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Computer science Correlation Feature extraction Feature selection Image analysis Image annotation Iterative algorithms Iterative methods Kernel Labels Learning Learning systems multi-label learning multi-view learning Noise measurement Regression analysis Regression models Representations sparse learning Task analysis
ISSN:	1520-9210, 1941-0077
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