The optimal distance measure for nearest neighbor classification 论文

摘要

A local distance measure is shown to optimize the performance of the nearest neighbor two-class classifier for a finite number of samples. The difference between the finite sample error and the asymptotic error is used as the criterion of improvement. This new distance measure is compared to the well-known Euclidean distance. An algorithm for practical implementation is introduced. This algorithm is shown to be computationally competitive with the present nearest neighbor procedures and is illustrated experimentally. A closed form for the corresponding second-order moment of this criterion is found. Finally, the above results are extended to