关键词: 关键词: 多核图聚类, 上界单纯形, 张量学习, 块对角性质, 高阶结构信息

Abstract: Abstract: Because multiple kernel learning can avoid selection of kernel function and parameter effectively, graph-based clustering can fully exploit complex structure information of datasets, so Multiple Kernel Graph Clustering (MKGC) had received widespread attention in recent years. However, both of them had demerits: graph-based learning would complicate the model, and the high rank of Laplacian matrix cannot ensure affinity graph contains c connected components accurately. Moreover, most of methods ignored the high order structure information among the candidate graphs, which made it difficult to fully utilize the multi-kernel information. Therefore, to tackle these problems, a novel MKGC method was proposed. To begin with, a new graph learning method based capped simplex projection was introduced to project kernel matrices onto graphs simplicity, which reduced the computational complexity. Meanwhile, used a new block diagonal constraint to keep the precise block diagonal property of learned affinity graphs; Moreover, low-rank tensor learning was introduced in capped simplex projection space to fully exploit the high-order structure information of multiple base kernels. Extensive experiments validate that proposed method has good clustering effect.

Key words: Multiple kernel graph clustering, capped simplex, tensor learning, block diagonal property, high-order affinity