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


  • Rongkai Xia Sun Yat-sen University
  • Yan Pan Sun Yat-sen University
  • Lei Du Sun Yat-sen University
  • Jian Yin Sun Yat-sen University



multi-view clustering, low-rank matrices, Markov chains


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 shared low-rank transition probabilitymatrix as a crucial input to the standard Markov chain methodfor clustering. The optimization problem of RMSC has a low-rankconstraint on the transition probability matrix, and simultaneouslya probabilistic simplex constraint on each of its rows. To solvethis challenging optimization problem, we propose an optimization procedurebased on the Augmented Lagrangian Multiplier scheme. Experimentalresults on various real world datasets show that theproposed method has superior performance over severalstate-of-the-art methods for multi-view clustering.




How to Cite

Xia, R., Pan, Y., Du, L., & Yin, J. (2014). Robust Multi-View Spectral Clustering via Low-Rank and Sparse Decomposition. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1).



Main Track: Novel Machine Learning Algorithms