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Hubness' is a recently discovered general problem of machine learning in high dimensional data spaces. Hub objects have a small distance to an exceptionally large number of data points, and anti-hubs are far from all other data points. It is related to the concentration of distances which impairs the contrast of distances in high dimensional spaces. Computation of secondary distances inspired by shared nearest neighbor (SNN) approaches has been shown to reduce hubness and concentration anddoi:10.1109/icdmw.2013.101 dblp:conf/icdm/FlexerS13 fatcat:pgbotxa5dvhwlo4juwhi7shpom