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I want to perform $k$-Nearest Neighbor Search in multidimensional space, but not using for example $L_2$-distance. I want the user to specify some "similar"-pairs examples and then perform a search using this information.

What algorithm can I use for this?

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  • $\begingroup$ Can you be a bit more specific regarding what type of metric you want to use? $\endgroup$
    – Pedro
    Commented Nov 20, 2012 at 14:11
  • $\begingroup$ @Pedro I don't know what metric I want, but I think it must depend on data. I don't know what "similar" mean, but I have pairs(examples) which are similar and I want to extract similarity metric from this data and then perform K-Nearest Neighbor Search. Maybe I can use metric like here slide 10 cui.unige.ch/AI-group/teaching/dmc/09-10/cours/dm16-ml.pdf $\endgroup$
    – mrgloom
    Commented Nov 21, 2012 at 14:05
  • $\begingroup$ @mrgloom So do you want to train a classifier and then apply that to determine neighbors? $\endgroup$ Commented Jan 7, 2013 at 14:24

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This functionality is implemented in the libAGF library. Check here: http://libagf.sourceforge.net/download.html To get an overview (the webpage is not very informative), download the library and check for the agf_paper in doc directory.

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    $\begingroup$ Is that paper also available separately, so that one doesn't have to download the library? $\endgroup$ Commented Nov 21, 2012 at 10:13

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