Learning kernels from indefinite similarities

  title={Learning kernels from indefinite similarities},
  author={Yihua Chen and Maya R. Gupta and Benjamin Recht},
Similarity measures in many real applications generate indefinite similarity matrices. In this paper, we consider the problem of classification based on such indefinite similarities. These indefinite kernels can be problematic for standard kernel-based algorithms as the optimization problems become non-convex and the underlying theory is invalidated. In order to adapt kernel methods for similarity-based learning, we introduce a method that aims to simultaneously find a reproducing kernel… CONTINUE READING


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