Regularized label relaxation with negative technique for image classification-Reference-Cited by-同舟云学术

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Regularized label relaxation with negative technique for image classification

Published:2022-05-18 Issue:28 Volume:81 Page:41131-41149
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

He Kai,Peng Yali,Liu Shigang,Li Jun

Funder

National Natural Science Foundation of Shaanxi Province

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-022-12417-x.pdf

Reference43 articles.

1. Bemporad A (2016) A quadratic programming algorithm based on nonnegative least squares with applications to embedded model predictive control. IEEE Trans Autom Control 61(4):1111–1116

2. Cao J, Lin Z (2015) Extreme learning machines on high dimensional and large data applications: a survey. Math Probl Eng 2015:1–13

3. Cao J, Zhao Y, Lai X et al (2015) Landmark recognition with sparse representation classification and extreme learning machine. J Franklin Institute Eng Appl Math 352(10):4528–4545

4. Chen C, He B, Ye Y et al (2016) The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent. Math Program 155(1):57–79

5. Cohen A, Migliorati G (2017) Optimal weighted least-squares methods. SMAI J Comput Math 3:181–203

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