Robust 2DPCA by Tℓ₁ Criterion Maximization for Image Recognition

Robust 2DPCA by Tℓ₁ Criterion Maximization for Image Recognition

Two-dimensional principal component analysis (2DPCA) has been widely used to extract image features. As opposed to PCA, 2DPCA directly treats 2D matrices to extract image features instead of transforming 2D matrices into vectors. However, the classical 2DPCA based on F-norm square is sensitive to no...

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Bibliographic Details
Main Authors: Xiangfei Yang, Wensi Wang, Liming Liu, Yuanhai Shao, Liting Zhang, Naiyang Deng
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Two-dimensional principal component analysis (2DPCA)
Tℓ₁ criterion
robust
dimensionality reduction
feature extraction
Online Access:https://ieeexplore.ieee.org/document/9316274/

Internet

https://ieeexplore.ieee.org/document/9316274/

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