Sparse Linear Discriminant Analysis With Constant Between-Class Distance for Feature Selection
TL;DR
Sparse Linear Discriminant Analysis with constant between-class distance enhances feature selection by maintaining discriminability. It builds on Trace Ratio LDA, addressing its computational challenges for improved performance.
Sparse Linear Discriminant Analysis With Constant Between-Class Distance for Feature Selection
Shuangle Guo; Yongxia Li; Jianguang Zhang; Yue Liu; Tian Tian; Mengchen Guo
https://doi.org/10.1109/ACCESS.2024.3514612
Volume 13
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 selection algorithms. As is known, TR-LDA is a challenging problem to solve because of its trace-ratio...