A multi-kernel spectral clustering algorithm based on incomplete views

With the diversity of data sources, multi-view clustering algorithms are widely used. The traditional research routinely assumes that the multi-view data is complete, but the existing data may actually be missing. So the incomplete view clustering has become a hot research topic. In this paper, a multi-kernel spectral clustering algorithm based on incomplete views (IVMKSpec) is put forward. Firstly, the incomplete datasets are constructed with 10% to 90% of the loss rate, where they are clustered with the estimation of kernel and spectral clustering, and then the clustering results are evaluated by NMI and F-measure. Multi-kernel learning overcomes the defect that single kernel can effectively not handle data of heterogeneous and multiple data sources. Moreover, the multi-kernel spectral clustering is applied to incomplete datasets which improves the performance of incomplete clustering. Finally, the experimental results demonstrate that the proposed algorithm is robust and effective in most datasets.