Year: 2008
Type: preprint
Abstract: We consider the problem of finding high dimensional approximate nearest neighbors. Suppose there are d independent rare features, each having its own independent statistics. A point x will have x_{i}=0 denote the absence of feature i, and x_{i}=1 its existence. Sparsity means that usually x_{i}=0. Distance between points is a variant of the Hamming distance. Dimensional reduction converts the sparse heterogeneous problem into a lower dimensional full homogeneous problem. However we will see that the converted problem can be much harder to solve than the original problem. Instead we suggest a direct approach. It consists of T tries. In try t we rearrange the coordinates in decreasing order of (1-r_{t,i})\frac{p_{i,11}}{p_{i,01}+p_{i,10}} \ln\frac{1}{p_{i,1*}} where 0
Source: arXiv (Cornell University)
Author: Dubiner, Moshe
Institution: Google (United States)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2949468641