均分式L1/2正则化稀疏表示特征选择方法

针对高维数据中出现的特征冗余问题,提出一种均分式L1/2正则化稀疏表示特征选择方法。根据特征数将高维数据集平均分成若干份,使用阈值迭代算法对每个特征子集进行L1/2正则化特征选择计算,聚合经过滤的数据集,运行L1/2正则化特征选择算法。该特征选择方法能够选择出更具代表性的特征,减少时间开销。实验结果表明,该方法适用于高维数据和低维数据。

Equational L1/2 regularization sparse representation feature selection

Aiming at the problem of feature redundancy in high-dimensional data, equational L1/2 regularization sparse representation feature selection was proposed. High-dimensional data sets were divided into several parts averagely according to the feature number, and selecting feature with L1/2 regularization was implemented using iterative half thresholding algorithm for each part, the filtered data set was aggregated and selecting feature with L1/2 regularization was implemented. This feature selection method can select more representative features and reduce the time cost. Experimental results show that the equational L1/2 regularization feature selection method is suitable for both high dimensional data and low dimensional data.