融合低维表示与模糊系统，提升特征选择精度与稳定性。
Low-Dimensional Representation-Driven TSK Fuzzy System for Feature Selection
- 用投影矩阵保留数据低维结构，避免信息回溯损失。
- 引入松弛机制和ℓ₂,₁-范数，显著提升特征筛选效果。
- 适合高维数据中关键特征提取，尤其适用于小样本场景。
特征选择可筛选重要特征以缓解维度灾难。子空间学习作为主流降维方法，能将原始数据投影至低维空间，但通常需将其转换回原空间，造成信息丢失。此外，基于门函数的Takagi-Sugeno-Kang模糊系统（TSK-FS）常缺乏判别力。为此，本文提出一种结合子空间学习与TSK-FS的新特征选择方法：首先利用投影矩阵拟合数据内在低维表示；随后将该低维表示输入TSK-FS评估其可用性，并通过松弛机制避免数值下溢；最后引入ℓ₂,₁-范数实现关键特征选择。在18个数据集上与6种先进方法对比，实验结果表明所提方法具有明显优势。
原文摘要 · Abstract (English)
Feature selection can select important features to address dimensional curses. Subspace learning, a widely used dimensionality reduction method, can project the original data into a low-dimensional space. However, the low-dimensional representation is often transformed back into the original space, resulting in information loss. Additionally, gate function-based methods in Takagi-Sugeno-Kang fuzzy system (TSK-FS) are commonly less discrimination. To address these issues, this paper proposes a novel feature selection method that integrates subspace learning with TSK-FS. Specifically, a projection matrix is used to fit the intrinsic low-dimensional representation. Subsequently, the low-dimensional representation is fed to TSK-FS to measure its availability. The firing strength is slacked so that TSK-FS is not limited by numerical underflow. Finally, the $\ell _{2,1}$-norm is introduced to select significant features and the connection to related works is discussed. The proposed method is evaluated against six state-of-the-art methods on eighteen datasets, and the results demonstrate the superiority of the proposed method.
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