基于聚类结构和局部相似性的 多视图隐空间聚类.

In recent years, with the diversification of data acquisition, multi-view learning has become more and more important. Most multi-view clustering methods obtain the similarity between samples through self-representation or local structures. However, these methods do not consider the influence of noise and the clustering structure of the overall sample. This way may lead to a large error in the clustering study. To address this issue, this paper proposes a Multiview Latent subspace Clustering with Cluster structure and Local similarity（MLC2L）, which combines shared information on different views and suppresses the presence of possible noise through latent representations. Besides, it simultaneously explores the clustering structure and local similarity in the latent space, so the samples can be promoted to achieve the purpose of homogeneous aggregation and heterogeneous separation. Further, this paper introduces an alternate direction iterative optimization algorithm to quickly solve the objective function. The experimental results in six real datasets show that the proposed method is optimal for five evaluation metrics on MSRC-v1, UCI, and 100Leaves, and four out of five metrics on 3Sources, WebKB, and Prokaryotic datasets. Extensive experimental results demonstrate the effectiveness of the MLC2L, which combines local structure and overall clustering structure, in multiview clustering tasks. [ABSTRACT FROM AUTHOR]