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Unsupervised feature selection for subspace learning is an effective dimensionality reduction strategy whose essence lies in representing the original space with a lower-dimensional subspace in the absence of label information. However, existing ...

In general, fully exploiting the local structure of the original data space can effectively improve the clustering performance of nonnegative matrix factorization (NMF). Therefore, graph-based NMF algorithms have been widely studied and applied. ...

Aiming at the problems of difficult medical treatment for urban residents, low efficiency of hospitals, imbalance of medical resources and difficulty to meet the demands of patients, this paper proposes an urban intelligent medical service system based on ...

This paper studies the current status and advance of study of password cracking. First, we give a classification for password cracking from different dimensions. We studies brute-force cracking, dictionary cracking and rainbow table cracking. We found ...