Robust Multi-View Spectral Clustering via Low-Rank and Sparse Decomposition

Multi-view clustering, which seeks a partition of the data inmultiple views that often provide complementary information to eachother, has received considerable attention in recent years. In reallife clustering problems, the data in each view may haveconsiderable noise. However, existing clustering methods blindlycombine the information from multi-view data with possiblyconsiderable noise, which often degrades their performance. In thispaper, we propose a novel Markov chain method for RobustMulti-view Spectral Clustering (RMSC). Our method has a flavor oflow-rank and sparse decomposition, where we firstly construct atransition probability matrix from each single view, and then usethese matrices to recover a…

• NS
  National Science Foundation
• NN
  National Natural Science Foundation of China

  Awards: 61033010, 61370021
• NS
  Natural Science Foundation of Guangdong Province

  Award: S2013010011905