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J. McNames, "A Fast NearestNeighbor Algorithm Based on a Principal Axis Search Tree," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 9, pp. 964976, September, 2001.  
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@article{ 10.1109/34.955110, author = {J. McNames}, title = {A Fast NearestNeighbor Algorithm Based on a Principal Axis Search Tree}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {23}, number = {9}, issn = {01628828}, year = {2001}, pages = {964976}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.955110}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  A Fast NearestNeighbor Algorithm Based on a Principal Axis Search Tree IS  9 SN  01628828 SP964 EP976 EPD  964976 A1  J. McNames, PY  2001 VL  23 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
—A new fast nearestneighbor algorithm is described that uses principal component analysis to build an efficient search tree. At each node in the tree, the data set is partitioned along the direction of maximum variance. The search algorithm efficiently uses a depthfirst search and a new elimination criterion. The new algorithm was compared to 16 other fast nearestneighbor algorithms on three types of common benchmark data sets including problems from time series prediction and image vector quantization. This comparative study illustrates the strengths and weaknesses of all of the leading algorithms. The new algorithm performed very well on all of the data sets and was consistently ranked among the top three algorithms.
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