Semi-supervised Feature Selection via Rescaled Linear Regression
Semi-supervised Feature Selection via Rescaled Linear Regression
复制标题
DOI：
10.24963/ijcai.2017/211
复制
发表时间：
2017-08
期刊：
影响因子：
--
通讯作者：
Xiaojun Chen;Guowen Yuan;F. Nie;J. Huang
中科院分区：
文献类型：
--
作者：
Xiaojun Chen;Guowen Yuan;F. Nie;J. Huang
随着复杂高维稀疏数据的快速增长，利用有标记和无标记数据进行特征选择的新方法的需求增加了。基于最小回归的特征选择方法通常学习一个投影矩阵，并使用该投影矩阵评估特征的重要性，这缺乏理论解释。此外，这些方法无法找到投影矩阵的全局解和稀疏解。在本文中，我们提出了一种新的半监督特征选择方法，它可以学习投影矩阵的全局解和稀疏解。新方法通过用一组比例因子重新调整最小二乘回归中的回归系数来扩展最小二乘回归模型，这些比例因子用于对特征进行排序。结果表明，新模型可以学习全局解和稀疏解。此外，比例因子的引入为我们为什么可以使用投影矩阵对特征进行排序提供了理论解释。提出了一种简单而有效的具有收敛性证明的算法来优化新模型。在八个真实数据集上的实验结果表明了该方法的优越性。
With the rapid increase of complex and highdimensional sparse data, demands for new methods to select features by exploiting both labeled and unlabeled data have increased. Least regression based feature selection methods usually learn a projection matrix and evaluate the importances of features using the projection matrix, which is lack of theoretical explanation. Moreover, these methods cannot find both global and sparse solution of the projection matrix. In this paper, we propose a novel semi-supervised feature selection method which can learn both global and sparse solution of the projection matrix. The new method extends the least square regression model by rescaling the regression coefficients in the least square regression with a set of scale factors, which are used for ranking the features. It has shown that the new model can learn global and sparse solution. Moreover, the introduction of scale factors provides a theoretical explanation for why we can use the projection matrix to rank the features. A simple yet effective algorithm with proved convergence is proposed to optimize the new model. Experimental results on eight real-life data sets show the superiority of the method.