Nearest neighbor search through function minimization

This paper describes a solution to the nearest neighbor problem. The proposed algorithm, which makes use of the triangle inequality property, is considered from a function minimization perspective. The distance function is regularized through the computation of distance to a reference point; an initial starting point is rapidly found, and used in an iterative refinement using search over a sorted list. The algorithm is described, and simulation results are provided that suggest better performance than that obtained with spatial partition techniques such as Elias and k-d tree, for moderate size point sets.

• Type: Conference
• Year: 2001
• Where: CCCG
• Authors: Chang Shu, Michael A. Greenspan, Guy Godin
• Keywords: CCCG 2001, CCCG 2007, Function Minimization Perspective, Nearest Neighbor Problem, Triangle Inequality Property