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Unsupervised Feature Selection Using Structured Self-Representation
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作者 yanbei liu Kaihua liu +2 位作者 Xiao Wang Changqing Zhang Xianchao Tang 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2018年第3期62-73,共12页
Unsupervised feature selection has become an important and challenging problem faced with vast amounts of unlabeled and high-dimension data in machine learning. We propose a novel unsupervised feature selection method... Unsupervised feature selection has become an important and challenging problem faced with vast amounts of unlabeled and high-dimension data in machine learning. We propose a novel unsupervised feature selection method using Structured Self-Representation( SSR) by simultaneously taking into account the selfrepresentation property and local geometrical structure of features. Concretely,according to the inherent selfrepresentation property of features,the most representative features can be selected. Mean while,to obtain more accurate results,we explore local geometrical structure to constrain the representation coefficients to be close to each other if the features are close to each other. Furthermore,an efficient algorithm is presented for optimizing the objective function. Finally,experiments on the synthetic dataset and six benchmark real-world datasets,including biomedical data,letter recognition digit data and face image data,demonstrate the encouraging performance of the proposed algorithm compared with state-of-the-art algorithms. 展开更多
关键词 unsupervised feature selection local geometrical structure self-representation property high-dimension data
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