Adaptive Unsupervised Feature Selection With Structure Regularization
Adaptive Unsupervised Feature Selection With Structure Regularization
复制标题
具有结构正则化的自适应无监督特征选择
DOI：
10.1109/tnnls.2017.2650978
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发表时间：
2018-04-01
影响因子：
10.4
通讯作者：
Zheng, Qinghua
中科院分区：
文献类型：
--
作者：
Luo, Minnan;Nie, Feiping;Zheng, Qinghua
特征选择因其高效性和可解释性是最重要的降维技术之一。由于大规模的实际数据通常是在无标签的情况下收集的，并且给这些数据打标签极其昂贵且耗时，无监督特征选择已成为一个普遍存在且具有挑战性的问题。在没有标签信息的情况下，无监督特征选择的根本问题在于如何刻画原始特征空间的几何结构并生成一个能准确保留内在结构的可靠特征子集。在本文中，我们通过一个自适应重构图来刻画内在局部结构，并通过对相应的拉普拉斯矩阵施加秩约束来同时考虑其多连通分量（多簇）结构。为了得到一个理想的特征子集，我们同时学习最优重构图和选择矩阵，而不是使用一个预先确定的图。我们利用一种高效的交替优化算法来解决所提出的具有挑战性的问题，并对其收敛性和计算复杂性进行理论分析。最后，在几个基准数据集上针对聚类任务进行了大量实验，以验证所提出的无监督特征选择算法的有效性和优越性。
Feature selection is one of the most important dimension reduction techniques for its efficiency and interpretation. Since practical data in large scale are usually collected without labels, and labeling these data are dramatically expensive and time-consuming, unsupervised feature selection has become a ubiquitous and challenging problem. Without label information, the fundamental problem of unsupervised feature selection lies in how to characterize the geometry structure of original feature space and produce a faithful feature subset, which preserves the intrinsic structure accurately. In this paper, we characterize the intrinsic local structure by an adaptive reconstruction graph and simultaneously consider its multiconnected-components (multicluster) structure by imposing a rank constraint on the corresponding Laplacian matrix. To achieve a desirable feature subset, we learn the optimal reconstruction graph and selective matrix simultaneously, instead of using a predetermined graph. We exploit an efficient alternative optimization algorithm to solve the proposed challenging problem, together with the theoretical analyses on its convergence and computational complexity. Finally, extensive experiments on clustering task are conducted over several benchmark data sets to verify the effectiveness and superiority of the proposed unsupervised feature selection algorithm.