Spectral clustering with adaptive similarity measure in Kernel space

The similarity measure for complex data may not precisely reflect the true data structure, which leads to suboptimal clustering performance for spectral clustering. In this paper, we propose a novel spectral clustering method which measures the similarity of data points based on the adaptive neighborhood in Kernel space. In Kernel space, by assigning the adaptive and optimal neighbors for each data point based on the local structure, the proposed method learns a sparse matrix as the similarity matrix for spectral clustering. The proposed method is able to explore the underlying similarity relationships between data points, and is robust to the complex data. To validate the efficacy of the proposed method, we perform experiments on both synthetic and real datasets in comparison with some existing spectral clustering methods. The experimental results demonstrate that the proposed method obtains quite promising clustering performance.