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Subspace clustering (SC) is a promising technology involving clusters that are identified based on their association with subspaces in high-dimensional spaces. SC can be classified into hard subspace clustering (HSC) and soft subspace clustering (SSC). While HSC algorithms have been studied extensively and are well accepted by the scientific community, SSC algorithms are relatively new. However, as...
Soft subspace clustering algorithms have been successfully used for high dimensional data in recent years. However, the existing algorithms often utilize only one distance function to evaluate the distance between data items on each feature, which cannot deal with datasets with complex inner structures. In this paper, a composite kernel space (CKS) is constructed based on a set of basis kernels and...
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