A correlation analysis framework via joint sample and feature selection-Reference-Cited by-同舟云学术

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A correlation analysis framework via joint sample and feature selection

Published:2022-12-24 Issue:13 Volume:82 Page:19539-19555
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Qiang Na,Shen Xiangjun,Ganaa Ernest Domanaanmwi,Yang Yang,Wu Shengli,Zhao Zengmin,Huang Shucheng

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/s11042-022-14237-5.pdf

Reference36 articles.

1. Akaho S. (2006) A kernel method for canonical correlation analysis. https://doi.org/10.48550/arXiv.cs/0609071

2. Arenasgarcia J, Petersen K, Hansen LK (2013) Kernel multivariate analysis framework for supervised subspace learning: a tutorial on linear and kernel multivariate methods. IEEE Signal Proc Mag 30(4):16–29

3. Apasiba Abeo T, Shen X-J, Bao B-K, Zha Z-J, Fan J A generalized multi-dictionary least squares framework regularized with multi-graph embeddings, vol 90

4. Belhumeur PN, Kriegman DJ (1996) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. In: European conference on computer vision

5. Cai D, He X, Han J (2007) Spectral regression: A unified approach for sparse subspace learning. In: Seventh IEEE international conference on data mining (ICDM 2007), IEEE, 2007, pp. 73–82

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