Robust Ordinal Embedding from Contaminated Relative Comparisons


  • Ke Ma Chinese Academy of Sciences
  • Qianqian Xu Chinese Academy of Sciences
  • Xiaochun Cao Chinese Academy of Sciences



Existing ordinal embedding methods usually follow a twostage routine: outlier detection is first employed to pick out the inconsistent comparisons; then an embedding is learned from the clean data. However, learning in a multi-stage manner is well-known to suffer from sub-optimal solutions. In this paper, we propose a unified framework to jointly identify the contaminated comparisons and derive reliable embeddings. The merits of our method are three-fold: (1) By virtue of the proposed unified framework, the sub-optimality of traditional methods is largely alleviated; (2) The proposed method is aware of global inconsistency by minimizing a corresponding cost, while traditional methods only involve local inconsistency; (3) Instead of considering the nuclear norm heuristics, we adopt an exact solution for rank equality constraint. Our studies are supported by experiments with both simulated examples and real-world data. The proposed framework provides us a promising tool for robust ordinal embedding from the contaminated comparisons.




How to Cite

Ma, K., Xu, Q., & Cao, X. (2019). Robust Ordinal Embedding from Contaminated Relative Comparisons. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 7908-7915.



AAAI Technical Track: Reasoning under Uncertainty