Angular Embedding: A Robust Quadratic Criterion

  title={Angular Embedding: A Robust Quadratic Criterion},
  author={Stella Yu},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
Given the size and confidence of pairwise local orderings, angular embedding (AE) finds a global ordering with a near-global optimal eigensolution. As a quadratic criterion in the complex domain, AE is remarkably robust to outliers, unlike its real domain counterpart LS, the least squares embedding. Our comparative study of LS and AE reveals that AE's robustness is due not to the particular choice of the criterion, but to the choice of representation in the complex domain. When the embedding is… CONTINUE READING
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