A Novel MVTLR-HCFS Algorithm for High Dimensional Data Clustering

With the rapid development of hardware and software technology, modern industry has produced a large amount of high dimensional unlabeled data, such as pictures and videos. As clusters of these data sets may exist in some subspaces, traditional algorithms are no longer applicable. The related algorithms based on sparse subspace could find clusters in the subspace, which solves the problem of high data dimension. However, these clustering process usually based on only one single view feature, which may affects their performance particularly sensitive to one single view. Inspired by the integrated algorithm, a large number of multi-view methods began to emerge. These methods could improve the clustering performance to some extent by integrating the subspace expressions of multiple views, conversely, the complementary information of multiple views is not fully considered. In addition, the problem of non-uniform distribution in clusters also exists in high dimensional data sets. Therefore, in this paper, on the basis of multi-view subspace method, a clustering algorithm based on tensor low rank expression is proposed to solve the problem of high dimensional datasets.