Fast Density Estimation for Approximated k Nearest Neighbor Classification

摘要

We propose a method for fast density estimation of samples, which makes it possible to significantly accelerate classification based on the k nearest neighbor (kNN) method. Our main premise is that many trials of a rough estimation of probability density function are conducted, and they are integrated by Bayesȁ9; theorem. The experimental results indicated that the classification time used in our method was at least 30 times faster than that of kNN.