| 摘要 |
In order to be able to handle very large numbers of tracks in a similarity determination in order to identify tracks similar to a predetermined track, a filtering method is used in order to identify a number of closest neighbor candidates between which the correct nearest neighbors are determined. Thus, the computationally heavy similarity determination is performed only on a subset of the tracks. This filtering step may be a fastmap determination of the tracks where the pivot points are determined not as the extreme points along the to individual dimension but at the median thereof in order to avoid extremely high divergence values. This helps preserving the neighborhoods. Also, the mapping is performed on the basis of a square-rooted Symmetric Kullback-Leibler (SKL) divergence which is more metric than the SKL and thus provides a better mapping.
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