基于图形正则化低秩表示张量与亲和矩阵的多视图聚类

针对聚类中忽略局部结构、 低秩表示张量与亲和矩阵高度依赖性等问题, 提出一种基于图形正则化低秩表示张量与亲和矩阵的多视图聚类方法. 首先, 提出一个统一的框架学习多视图子空间的图正则低秩表示张量和亲和矩阵; 其次, 进一步通过基于张量核范数的张量奇异值分解分析高阶交叉视图关联性, 并利用图形正则化保留嵌入在高维空间中的局部结构; 最后, 利用约束二次规划为每个视图分配自适应权重. 在7个数据集上的实验结果证明了该方法聚类效果更好.

Multiple View Clustering Based on Graph Regularization Low Rank Representation Tensor and Affinity Matrix

Aiming at the problems of  ignoring local structure, the high dependence of low rank representation tensor and affinity matrix,  we proposed a multiple view clustering method based on graph regularization， low rank representation tensor and affinity matrix. Firstly, we proposed a unified framework to learn the graph regular low rank representation tensor and affinity matrix of multiple view subspace. Secondly, furthermore, the correlation of high-order cross views was analyzed by tensor singular value decomposition based on tensor kernel norm, and the local structure embedded in high-dimensional space was preserved by graph regularization. Finally, constrained quadratic programming was used to assign adaptive weights to each view. Experimental results on seven data sets show that the  clustering effect of the proposed method is better.