Robust Multi-view Subspace Learning Through Structured Low-Rank Matrix Recovery
Multi-view data exists widely in our daily life.A popular approach to deal with multi-view data is the multi-view subspace learning(MvSL),which projects multi-view data into a common latent subspace to learn more powerful representation.Low-rank representation(LRR)in recent years has been adopted to design MvSL methods.Despite promising results obtained on real applications,existing methods are incapable of handling the scenario when large view divergence exists among multi-view data.To tackle this problem,we propose a novel framework based on structured low-rank matrix recovery.Specifically,we get rid of the framework of graph embedding and introduce class-label matrix to flexibly design a supervised low-rank model,which successfully learns a discriminative common subspace and discovers the invariant features shared by multi-view data.Experiments conducted on CMU PIE show that the proposed method achieves the state-of-the-art performance.Performance comparison under different random noise disturbance is also given to illustrate the robustness of our model.
Subspace learning Multi-view learning Low-rank representation
Jiamiao Xu Xinge You Qi Zheng Fangzhao Wang Peng Zhang
Huazhong University of Science and Technology,Wuhan 430074,China Research Institute of Huazhong University of Science and Technology in Shenzhen,Shenzhen,China;Huazh
国际会议
广州
英文
427-439
2018-11-23（万方平台首次上网日期，不代表论文的发表时间）