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Copyright of Basic Sciences Journal of Textile Universities / Fangzhi Gaoxiao Jichu Kexue Xuebao is the property of Basic Sciences Journal of Textile Universities and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Subspace clustering can usually handle high-dimensional data well, but due to the influence of the noise of the data itself, the block diagonal structure of the coefficient matrix is often easily destroyed. To solve the above problems, this paper proposed a semi-supervised sparse subspace clustering with label discrimination and local linear reinforcement. On the one hand, it better captured the global structure of the data by constraining the coefficient between the labeled data to be 0. On the other hand, it strengthened the local correlation between the neighboring points of the data through the K-nearest neighbor relationship, and eliminated a large number of unrelated data points enhance the robustness of the algorithm. The experiments on a variety of data verify the effectiveness of the proposed semi-supervised clustering algorithm. [ABSTRACT FROM AUTHOR]

In order to obtain a more reliable similarity matrix and make it contain the exact number of connected branches, this paper proposed a new sparse subspace clustering algorithm. The algorithm used the K nearest neighbor idea to find reliable neighbors from the local. In the aspect of distance metric, the algorithm selected the geodesic distance for calculation, and considered the geometric structure of the data in the high-dimensional space, which made the neighbor relationship of the data more reasonable. At the same time, using the Ky Fan theorem, the adaptive matrix adjusted the parameters so that the similar matrix contained the exact number of connected branches. In addition, the algorithm broke the conventional two-step mode, and simultaneously performed similar matrix learning and spectral clustering process, which closely linked data similarity and segmentation, further strengthening the mining and utilization of data structure information. The results of the experiments on artificial datasets, image datasets and real datasets show that the algorithm is effective. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In view of the large number of unlabeled high-dimensional data, this paper studied unsupervised feature selection in machine learning, and proposed an unsupervised feature selection algorithm combining self-representation similarity matrix and manifold learning. Firstly, it constructed the similarity matrix by the self-expressiveness property of the data. Based on the idea that low-dimensional manifold could represent high-dimensional data structures, it established an unsupervised feature selection optimization model considering manifold learning. Secondly, in order to ensure the selection of more useful and sparse features, it used the 12, 1 norm to constrain the optimization model, so that features competed with each other and eliminated redundancy. Furthermore, it solved the optimization model by alternating iteration of variables, and proved convergence of the algorithm. Finally, it demonstrated the effectiveness of the proposed algorithm by comparison with other unsupervised feature algorithms on four data sets. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Basic Sciences Journal of Textile Universities / Fangzhi Gaoxiao Jichu Kexue Xuebao is the property of Basic Sciences Journal of Textile Universities and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Basic Sciences Journal of Textile Universities / Fangzhi Gaoxiao Jichu Kexue Xuebao is the property of Basic Sciences Journal of Textile Universities and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)