关键词: 特征选择, Laplacian正则化项, Lasso, 支持向量机, 降维

Abstract: Among feature selections, Lasso method has been widely studied and applied. However, a main disadvantage of Lasso method is that it only considers the relationship between subject and label, and ignores the distribution information of subjects which may help to induce more discriminative features. To address this problem, this paper proposes a new Laplacian-based feature selection method called Lap-Lasso which can simultaneously achieve feature selection and preserve the intrinsic relatedness among subjects. Specifically, two regularization items are included in the proposed model. The first item is sparsity regularizer which ensures only a small number of features to be selected. In addition, to capture the intrinsic relatedness among subjects, it introduces a new Laplacian-based regularization item, which help to induce more discriminative features. Experimental results on UCI datasets show that the proposed algorithm can achieve better performances than conventional feature selection algorithms.

Key words: feature selection, Laplacian regularization, Lasso, support vector machine, dimensionality reduction