Global and local multi-view multi-label learning with incomplete views and labels-Reference-Cited by-同舟云学术

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Global and local multi-view multi-label learning with incomplete views and labels

Published:2020-04-08 Issue:18 Volume:32 Page:15007-15028
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Zhu Changming,Wang Panhong,Ma Lin,Zhou Rigui,Wei Lai

Funder

Chenguang Program

Young Scientists Fund

Postdoctoral Research Foundation of China

Natural Science Foundation of Shanghai

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/content/pdf/10.1007/s00521-020-04854-2.pdf

Reference34 articles.

1. Jiang H, Yi J, Chen S, Zhu X (2016) A multi-objective algorithm for task scheduling and resource allocation in cloud-based disassembly. J Manuf Syst 41:239–255

2. Fan QQ, Wang WL, Yan XF (2017) Multi-objective differential evolution with performance-metric-based self-adaptive mutation operator for chemical and biochemical dynamic optimization problems. Appl Soft Comput 59:33–44

3. Fan P, Zhou RG, Jing N, Li HS (2016) Geometric transformations of multidimensional color images based on NASS. Inf Sci 340:191–208

4. Zhu XF, Li XL, Zhang SC (2016) Block-row sparse multiview multilabel learning for image classification. IEEE Trans Cybern 46(2):450–461

5. Luo Y, Tao DC, Xu C, Xu C, Liu H, Wen YG (2013) Multiview vector-valued manifold regularization for multilabel image classification. IEEE Trans Neural Netw Learn Syst 24(5):709–722

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4. Towards deeper match for multi-view oriented multiple kernel learning;Pattern Recognition;2023-02

5. Label recovery and label correlation co-learning for multi-view multi-label classification with incomplete labels;Applied Intelligence;2022-08-09

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