D2KE: From Distance to Kernel and Embedding

  title={D2KE: From Distance to Kernel and Embedding},
  author={Lingfei Wu and Ian En-Hsu Yen and Fangli Xu and Pradeep Ravikumar and Michael Witbrock},
For many machine learning problem settings, particularly with structured inputs such as sequences or sets of objects, a distance measure between inputs can be specified more naturally than a feature representation. However, most standard machine models are designed for inputs with a vector feature representation. In this work, we consider the estimation of a function f : X → R based solely on a dissimilarity measure d : X × X → R between inputs. In particular, we propose a general framework to… CONTINUE READING


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