一种面向高维数据的DS-ALasso变量选择方法

Variable selection is of great importance in High-dimensional data analysis.The Laaso method does not have the Oracle property and can only select the disadvantages of a variable when dealing with the data structure with the dimension larger than the sample size.To address this issue, this paper proposes an improved Lasso method, called DS-ALasso. The combination of Dantzig Selector and Adaptive Lasso can be an effective method which can reduce the dimensionality of high-dimensional data to less than the sample size and have the property of Oracle. Experimental results show that the DS-ALasso.as an effective feature selection method. can effectively deal with the high-dimensional and large sample datasets.