An Overview of the New Feature Selection Methods in Finite Mixture of Regression Models
An Overview of the New Feature Selection Methods in Finite Mixture of Regression Models
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发表时间：
2011-11
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通讯作者：
Abbas Khalili
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作者：
Abbas Khalili
变量（特征）选择在当代统计学习和科学研究中备受关注。这主要是由于现代技术的快速发展，使科学家能够收集前所未有的规模和复杂性的数据。这类应用程序中的一类统计问题涉及将输出变量建模为大量特征的一个小子集的函数。在某些应用中，数据样本甚至可能来自多个子种群。在这些情况下，为每个子群体选择正确的预测特征（变量）是至关重要的。经典的最佳子集选择方法对于许多现代统计应用来说计算代价太大。在过去的十年中，新的变量选择方法已经成功地开发出来，以处理大量的变量。它们被设计用于同时选择重要变量并在统计模型中估计其影响。在本文中，我们概述了有限混合回归模型中变量选择问题的理论、方法和实现的最新发展。
Variable (feature) selection has attracted much attention in contemporary statistical learning and recent scientific research. This is mainly due to the rapid advancement in modern technology that allows scientists to collect data of unprecedented size and complexity. One type of statistical problem in such applications is concerned with modeling an output variable as a function of a small subset of a large number of features. In certain applications, the data samples may even be com- ing from multiple subpopulations. In these cases, selecting the correct predictive features (variables) for each subpopulation is crucial. The classical best subset selection methods are computationally too expen- sive for many modern statistical applications. New variable selection methods have been successfully developed over the last decade to deal with large numbers of variables. They have been designed for simulta- neously selecting important variables and estimating their effects in a statistical model. In this article, we present an overview of the recent developments in theory, methods, and implementations for the variable selection problem in finite mixture of regression models.