Sparse Linear Discriminant Analysis With Constant Between-Class Distance for Feature Selection

Sparse Linear Discriminant Analysis With Constant Between-Class Distance for Feature Selection

Feature selection is an important preprocessing step in machine learning to remove irrelevant and redundant features. Due to its ability to effectively maintain the discriminability of extracted features, Trace Ratio Linear Discriminant Analysis (TR-LDA) has become the foundation for many feature se...

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Bibliographic Details
Main Authors: Shuangle Guo, Yongxia Li, Jianguang Zhang, Yue Liu, Tian Tian, Mengchen Guo
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Feature selection
TR-LDA
sparse regression
Online Access:https://ieeexplore.ieee.org/document/10788680/
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