Li Zhengxin, Nie Feiping, Wu Danyang, Wang Zheng, Li Xuelong
IEEE Trans Cybern. 2024 Apr;54(4):2420-2433. doi: 10.1109/TCYB.2023.3264907. Epub 2024 Mar 18.
Classification is a fundamental task in the field of data mining. Unfortunately, high-dimensional data often degrade the performance of classification. To solve this problem, dimensionality reduction is usually adopted as an essential preprocessing technique, which can be divided into feature extraction and feature selection. Due to the ability to obtain category discrimination, linear discriminant analysis (LDA) is recognized as a classic feature extraction method for classification. Compared with feature extraction, feature selection has plenty of advantages in many applications. If we can integrate the discrimination of LDA and the advantages of feature selection, it is bound to play an important role in the classification of high-dimensional data. Motivated by the idea, we propose a supervised feature selection method for classification. It combines trace ratio LDA with l -norm regularization and imposes the orthogonal constraint on the projection matrix. The learned row-sparse projection matrix can be used to select discriminative features. Then, we present an optimization algorithm to solve the proposed method. Finally, the extensive experiments on both synthetic and real-world datasets indicate the effectiveness of the proposed method.
分类是数据挖掘领域的一项基本任务。不幸的是，高维数据常常会降低分类性能。为了解决这个问题，降维通常被用作一种重要的预处理技术，它可以分为特征提取和特征选择。由于具有获得类别判别能力，线性判别分析（LDA）被公认为一种用于分类的经典特征提取方法。与特征提取相比，特征选择在许多应用中具有诸多优势。如果我们能够将LDA的判别能力与特征选择的优势相结合，那么它必然会在高维数据分类中发挥重要作用。受此想法的启发，我们提出了一种用于分类的监督特征选择方法。它将迹比LDA与l -范数正则化相结合，并对投影矩阵施加正交约束。所学习到的行稀疏投影矩阵可用于选择判别性特征。然后，我们提出一种优化算法来求解所提出的方法。最后，在合成数据集和真实世界数据集上进行的大量实验表明了所提出方法的有效性。
