基于多样性的一致谱嵌入学习

Consensus Spectral Embedding Learning Based on Diversity

针对如何将多视图的丰富信息融合进一致图以及避免谱嵌入后续处理过程中导致的次优性能问题,提出一种基于多样性的一致谱嵌入学习的多视图聚类算法.该算法在考虑视图多样性的前提下自动学习权重以便更好地学习一致图,并学习一致的谱嵌入矩阵和离散化聚类标签矩阵.通过在真实数据集上与其他算法进行对比实验,证明了该算法在提升聚类性能方面的优越性.

Aiming at the problems that how to fuse the rich information of multi-views into the consensus graph and avoid the sub-optimal performance caused by the spectral embedding in the subsequent processing, we proposed a multi-view clustering algorithm based on diversity consensus spectral embedding learning. In order to learn the consensus graph better, the algorithm automatically learnt the consensus spectral embedding matrix and the discrete clustering label matrix under the consideration of the diversity of views. By comparing with other algorithms on real data sets, the superiority of the proposed algorithm in improving clustering performance was proved.