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On the Surprising Behavior of Distance Metrics in High ...
bib.dbvis.defundamental than the performance degradation of high dimensional algorithms. In most high dimensional applications the choice of the distance metric is not obvious; and the notion for the calculation of similarity is very heuristical. Given the non-contrasting nature of the distribution of distances to a given
Visualizing Data using t-SNE - Laurens van der Maaten
lvdmaaten.github.iohigh-dimensional space, σi). The remaining parameter to be selected is the variance σi of the Gaussian that is centered over each high-dimensional datapoint, xi. It is not likely that there is a single value of σi that is optimal for all datapoints in the data set because the density of the data is likely to vary. In dense regions,