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

• Published in: IEEE transactions on multimedia Vol. 22; no. 11; pp. 2844 - 2857
• Main Authors: Zhang, Yongshan, Wu, Jia, Cai, Zhihua, Yu, Philip S.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.11.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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

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...