标题： 基于正则化方法的模型聚类和判别分析中的变量选择 显示英文标题

标题： Variable selection in model-based clustering and discriminant analysis with a regularization approach

摘要： 在模型聚类和分类中已经提出了与变量选择相关的方法。 这些方法利用向后或向前的过程来定义变量的角色。 不幸的是，这些逐步程序非常缓慢，使得这些变量选择算法在处理大数据集时效率低下。 本文提出了一种用于模型聚类和分类的替代变量选择正则化方法。 在此方法中，变量首先通过类似于套索的过程进行排序，以避免痛苦的逐步算法。 因此，Maugis等人（2009b）的变量选择方法可以有效地应用于高维数据集。 显示英文摘要

摘要： Relevant methods of variable selection have been proposed in model-based clustering and classification. These methods are making use of backward or forward procedures to define the roles of the variables. Unfortunately, these stepwise procedures are terribly slow and make these variable selection algorithms inefficient to treat large data sets. In this paper, an alternative regularization approach of variable selection is proposed for model-based clustering and classification. In this approach, the variables are first ranked with a lasso-like procedure in order to avoid painfully slow stepwise algorithms. Thus, the variable selection methodology of Maugis et al (2009b) can be efficiently applied on high-dimensional data sets.