Feature selection has attracted a great deal of interest over the past decades. By selecting meaningful feature subsets, the performance of learning algorithms can be effectively improved. Because label information is expensive to obtain, unsupervised feature selection methods are more widely used than the supervised ones. The key to unsupervised feature selection is to find features that effectively reflect the underlying data distribution. However, due to the inevitable redundancies and noise in a dataset, the intrinsic data distribution is not best revealed when using all features. To address this issue, we propose a novel unsupervised feature selection algorithm via joint local learning and group sparse regression (JLLGSR). JLLGSR incorporates local learning based clustering with group sparsity regularized regression in a single formulation, and seeks features that respect both the manifold structure and group sparse structure in the data space. An iterative optimization method is developed in which the weights finally converge on the important features and the selected features are able to improve the clustering results. Experiments on multiple real-world datasets (images, voices, and web pages) demonstrate the effectiveness of JLLGSR.
The dynamic modeling and solution of the 3-■RS spatial parallel manipulators with flexible links were investigated.Firstly,a new model of spatial flexible beam element was proposed,and the dynamic equations of elements and branches of the parallel manipulator were derived.Secondly,according to the kinematic coupling relationship between the moving platform and flexible links,the kinematic constraints of the flexible parallel manipulator were proposed.Thirdly,using the kinematic constraint equations and dyna... 相似文献
