Lei Ling1, Lijun Huang2, Jie Wang2, Li Zhang2, Yue Wu2, Yizhang Jiang1, Kaijian Xia2,3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2353-2379, 2023, DOI:10.32604/cmes.2023.028828
Abstract In recent years, the soft subspace clustering algorithm has shown good results for high-dimensional data, which
can assign different weights to each cluster class and use weights to measure the contribution of each dimension
in various features. The enhanced soft subspace clustering algorithm combines interclass separation and intraclass
tightness information, which has strong results for image segmentation, but the clustering algorithm is vulnerable
to noisy data and dependence on the initialized clustering center. However, the clustering algorithm is susceptible to
the influence of noisy data and reliance on initialized clustering centers and falls into a local optimum; the clustering
effect… More >
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