Enumerate Lasso Solutions for Feature Selection
Enumerate Lasso Solutions for Feature Selection
复制标题
枚举用于特征选择的套索解决方案
DOI：
10.1609/aaai.v31i1.10793
复制
发表时间：
2017
影响因子：
2.7
通讯作者：
Takanori Maehara
中科院分区：
文献类型：
--
作者：
Satoshi Hara;Takanori Maehara
我们提出了一种Lasso回归问题的枚举解算法。在普通Lasso回归中，获得一个全局最优，并将结果特征解释为任务相关特征。然而，这可能忽略了套索没有选择的可能相关的特性。利用该方法，我们可以列举许多可能的特征集供人工检测，从而记录所有重要的特征。我们证明了与普通Lasso相比，通过枚举解可以在更少的限制条件下精确地恢复真特征集。我们在数值模拟中也证实了我们的理论结果。最后，在基因表达和文本数据中，我们证明了该方法可以枚举出各种有意义的特征集，而这些特征集被全局最优算法所忽略。
We propose an algorithm for enumerating solutions to the Lasso regression problem.In ordinary Lasso regression, one global optimum is obtained and the resulting features are interpreted as task-relevant features.However, this can overlook possibly relevant features not selected by the Lasso.With the proposed method, we can enumerate many possible feature sets for human inspection, thus recording all the important features.We prove that by enumerating solutions, we can recover a true feature set exactly under less restrictive conditions compared with the ordinary Lasso.We confirm our theoretical results also in numerical simulations.Finally, in the gene expression and the text data, we demonstrate that the proposed method can enumerate a wide variety of meaningful feature sets, which are overlooked by the global optima.