PCCA: A new approach for distance learning from sparse pairwise constraints

  title={PCCA: A new approach for distance learning from sparse pairwise constraints},
  author={Alexis Mignon and Fr{\'e}d{\'e}ric Jurie},
  journal={2012 IEEE Conference on Computer Vision and Pattern Recognition},
This paper introduces Pairwise Constrained Component Analysis (PCCA), a new algorithm for learning distance metrics from sparse pairwise similarity/dissimilarity constraints in high dimensional input space, problem for which most existing distance metric learning approaches are not adapted. PCCA learns a projection into a low-dimensional space where the distance between pairs of data points respects the desired constraints, exhibiting good generalization properties in presence of high… CONTINUE READING
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