Efficient Online Relative Comparison Kernel Learning

  title={Efficient Online Relative Comparison Kernel Learning},
  author={Eric Heim and Matthew Berger and Lee M. Seversky and Milos Hauskrecht},
Learning a kernel matrix from relative comparison human feedback is an important problem with applications in collaborative filtering, object retrieval, and search. For learning a kernel over a large number of objects, existing methods face significant scalability issues inhibiting the application of these methods to settings where a kernel is learned in an online and timely fashion. In this paper we propose a novel framework called Efficient online Relative comparison Kernel LEarning (ERKLE… CONTINUE READING
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