CMvSC:知识迁移下的深度一致性多视图谱聚类网络

谱聚类是聚类分析中极具代表性的方法之一,由于其对数据结构没有太多假设要求,受到了研究者们的广泛关注.但传统的谱聚类算法通常受到谱嵌入的可扩展性和泛化性的限制,即:无法应对大规模设置和复杂数据分布.为克服以上缺陷,旨在引入深度学习框架提升谱聚类的泛化能力与可扩展能力,同时,结合多视图学习挖掘数据样本的多样性特征,从而提出...

CMvSC: Knowledge Transferring Based Deep Consensus Network for Multi-view Spectral Clustering

Spectral clustering, which is one of the most representative methods in clustering analysis, reveives much attention from scholars, because it does not constrain the data structure of the original samples. However, traditional spectral clustering algorithm usually contains two major limitations, i.e. it is unable to cope with the large-scale settings and complex data distribution. To overcome the above shortcomings, in this paper, we introduce a deep learning framework to improve the generalization and scalability of spectral clustering, and combine the multi-view learning to mine diverse features among data samples, finally propose a knowledge transferring based deep Consensus network for Multi-view Spectral Clustering (CMvSC). First, considering the local invariance of single view, CMvSC adopts the local learning layer to learn the specific embedding of each view individually; Then, because of the global consistency among multiple views, CMvSC introduces the global learning layer to achieve paramenter sharing and feature transferring, and learns the shared embedding in different views; Meanwhile, taking the effect of affinity matrix for spectral clustering into consideration, CMvSC learns the affinity correlation between the paired samples by training the siamese network and designing the contrastive loss, which replaces the distance metric in tranditional spectral clustering. Finally, the experimental results on four datasets demonstrate the effectiveness of our CMvSC for multi-view clustering.