The Effect of Points Dispersion on the k-nn Search in Random Projection Forests

Partitioning trees are efficient data structures for k-nearest neighbor search. Machine learning libraries commonly use a special type of partitioning trees called kd-trees to perform k-nn search. Unfortunately, kd-trees can be ineffective in high dimensions because they need more tree levels to decrease the vector quantization (VQ) error. Random projection trees rpTrees solve this scalability problem by using random directions to split the data. A collection of rpTrees is called rpForest. k-nn search in an rpForest is influenced by two factors: 1) the dispersion of points along the random direction and 2) the number of rpTrees in the rpForest. In this study, we investigate how these two factors affect the k-nn search with varying k values and different datasets. We found that with larger number of trees, the dispersion of points has a very limited effect on the k-nn search. One should use the original rpTree algorithm by picking a random direction regardless of the dispersion of points.

Automated summary

Partitioning trees are efficient for k-nearest neighbor search, but kd-trees can be ineffective in high dimensions. Random projection trees (rpTrees) solve this problem and are influenced by point dispersion and the number of rpTrees in an rpForest.