Unsupervised feature selection by manifold regularized self-representation
Siqi Liang, Qian Xu, Pengfei Zhu, Qinghua Hu, Changqing Zhang · 2017
Unsupervised feature selection has been proven to be an efficient technique in mitigating the curse of dimensionality. It helps to understand and analyze the prevalent high-dimensional unlabeled data. Recently, the self-similarity property of objects, which assumes that a feature can be represented by the linear combination of its relevant features, has been successfully used in unsupervised feature selection. However, it does not take the geometry structure of the sample space into consideration. In this paper, we propose a novel algorithm termed manifold regularized self-representation(MRSR). To preserve the local spatial structure, we incorporate an effective manifold regularization into the objective function. An iterative reweighted least square (IRLS) algorithm is developed to solve the optimization problem and the convergence is proved. Extensive experimental results on several benchmark datasets validate the effectiveness of the proposed method.