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

@article{Mignon2012PCCAAN,
  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},
  year={2012},
  pages={2666-2672}
}
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
Highly Influential
This paper has highly influenced 70 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 619 citations. REVIEW CITATIONS

8 Figures & Tables

Topics

Statistics

0501001502012201320142015201620172018
Citations per Year

619 Citations

Semantic Scholar estimates that this publication has 619 citations based on the available data.

See our FAQ for additional information.