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
The article explores some of the important features of pre-Qin Chinese rhetoric and challenges it poses to traditional Western rhetoric,with the former being seen as harmonic or self-effacing for its purpose and parad...The article explores some of the important features of pre-Qin Chinese rhetoric and challenges it poses to traditional Western rhetoric,with the former being seen as harmonic or self-effacing for its purpose and paradoxical for its epistemological underpinning.The author does not intend to suggest that the Chinese tradition is the right path to rhetoric,but at least it points to an alternative to approaching this language art as defined by Aristotle.展开更多
基金Sponsored by the Major Program of National Natural Science Foundation of China(Grant No.13&ZD162)the Applied Basic Research Programs of China National Textile and Apparel Council(Grant No.J201509)
文摘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.
文摘The article explores some of the important features of pre-Qin Chinese rhetoric and challenges it poses to traditional Western rhetoric,with the former being seen as harmonic or self-effacing for its purpose and paradoxical for its epistemological underpinning.The author does not intend to suggest that the Chinese tradition is the right path to rhetoric,but at least it points to an alternative to approaching this language art as defined by Aristotle.
