paper critique &summarization : Algorithms for Fast Vector Quantization

Title: Algorithms for Fast Vector Quantization
Author: Sunil Arya and David M. Mount

Summarization:
For fast vector quantization, this paper introduced three algorithm:
1.standard KD-tree with incremental distance calculation, which uses the information of distance between query and boundaries to decide which path could be stopped.
2.priority kD-tree search , which maintains a priority queue of subtrees to record the sibling of the node pass down and could find the really near point with query.
3. neighborhood graphs, which use the neighborhood graph to improve precision, it will expand query to the nearest neighbor and pass down until a point which all neighbor have been parsed, this method could get best performance of the three methods.

critique:
KD-tree based quantize algorithm is efficiency, but only in low dimension vector space, how to use these methods in high dimension and could reach nearly same performance is very important now.