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Distance metric learning, with application  (Make Corrections)  
to clustering with side-information Eric P. Xing, Andrew Y. Ng, Michael I....



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Abstract: Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many "plausible" ways, and if a clustering algorithm such as K-means initially fails to find one that is meaningful to a user, the only recourse may be for the user to manually tweak the metric until sufficiently good clusters are found. For these and other applications requiring good metrics, it is desirable that we provide a more systematic way for users to... (Update)

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BibTeX entry:   (Update)

@misc{ with-distance,
  author = "To Clustering With",
  title = "Distance Metric Learning, With Application",
  url = "citeseer.ist.psu.edu/752755.html" }
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