Fast Overlapping Group Lasso
Fast Overlapping Group Lasso
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发表时间：
2010-09
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通讯作者：
Jun Liu;Jieping Ye
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作者：
Jun Liu;Jieping Ye
Lasso组是Lasso的扩展，用于在（预定义的）非重叠特征组上进行特征选择。不重叠的集团结构限制了其在实践中的适用性。最近有几次尝试研究一种更一般的提法，其中给出了各组特征，各组之间可能有重叠。然而，由于组重叠，所得到的优化解决起来更具挑战性。本文考虑重叠群Lasso惩罚问题的有效优化。我们揭示了与重叠组Lasso相关的邻近算子的几个关键性质，并通过求解光滑凸对偶问题来计算邻近算子，该问题允许使用梯度下降型算法进行优化。我们使用乳腺癌基因表达数据集进行了经验评估，该数据集由8，141个基因组成（重叠）基因集。实验结果证明了该算法的有效性和实用性。
The group Lasso is an extension of the Lasso for feature selection on (predefined) non-overlapping groups of features. The non-overlapping group structure limits its applicability in practice. There have been several recent attempts to study a more general formulation, where groups of features are given, potentially with overlaps between the groups. The resulting optimization is, however, much more challenging to solve due to the group overlaps. In this paper, we consider the efficientoptimization of the overlapping group Lasso penalized problem. We reveal several key properties of the proximal operator associated with the overlapping group Lasso, and compute the proximal operator by solving the smooth and convex dual problem, which allows the use of the gradient descent type of algorithms for the optimization. We have performed empirical evaluations using the breast cancer gene expression data set, which consists of 8,141 genes organized into (overlapping) gene sets. Experimental results demonstrate the efficiency and effectiveness of the proposed algorithm.