Manifold-based denoising, outlier detection, and dimension reduction algorithm for high-dimensional data-Reference-Cited by-同舟云学术

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Manifold-based denoising, outlier detection, and dimension reduction algorithm for high-dimensional data

Published:2023-06-09 Issue:11 Volume:14 Page:3923-3942
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Zhao Guanghua,Yang Tao^ORCID,Fu Dongmei

Funder

Science and Technology Basic Resources Investigation Project

National Natural Science Foundation of China

Scientific and Technological Innovation Foundation of Shunde Graduate School

Scientific and Technological Innovation Foundation of Foshan Municipal People’s Government

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s13042-023-01873-y.pdf

Reference39 articles.

1. Izenman AJ (2012) Introduction to manifold learning[J]. Wiley Interdiscip Rev Comput Stat 4(5):439–446

2. Ayala D, Francis J (2015) Factorization homology of topological manifolds[J]. J Topol 8(4):1045–1084

3. Huang L, Lu J, Tan YP (2014) Multi-manifold metric learning for face recognition based on image sets[J]. J Vis Commun Image Represent 7(25):1774–1783

4. Wang B, Sun Y, Chu Y et al (2022) Refining electronic medical records representation in manifold subspace[J]. Bioinformatics 23(1):1–17

5. Jiang Q, Jia M, Hu J et al (2009) Machinery fault diagnosis using supervised manifold learning[J]. Mech Syst Signal Process 23(7):2301–2311

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