Robust and Sparse Kernel-Free Quadratic Surface LSR via L<sub>2,p</sub>-Norm With Feature Selection for Multi-Class Image Classification

Robust and Sparse Kernel-Free Quadratic Surface LSR via L<sub>2,p</sub>-Norm With Feature Selection for Multi-Class Image Classification

Least Squares Regression (LSR) is a powerful machine learning method for image classification and feature selection. In this study, a framework approach is introduced for the multi-classification problem based on the <inline-formula> <tex-math notation="LaTeX">$L_{2,p}$ </te...

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Bibliographic Details
Main Authors: Yongqi Zhu, Zhixia Yang, Junyou Ye, Yongxing Hu
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Multi-class classification learning
Manifold regularization
Sparse learning
Kernel-free
L2,p-norm
Online Access:https://ieeexplore.ieee.org/document/10848070/
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