Survey of Nearest Neighbor Techniques

TL;DR

This survey reviews various nearest neighbor techniques, highlighting their advantages and limitations, and categorizes methods into structureless and structure-based approaches to address issues like memory and computational complexity.

Contribution

It provides a comprehensive classification and comparison of existing NN techniques, including recent developments and their respective strengths.

Findings

Structureless methods overcome memory limitations.

Structure-based techniques reduce computational complexity.

Various NN techniques are suited for different applications.

Abstract

The nearest neighbor (NN) technique is very simple, highly efficient and effective in the field of pattern recognition, text categorization, object recognition etc. Its simplicity is its main advantage, but the disadvantages can't be ignored even. The memory requirement and computation complexity also matter. Many techniques are developed to overcome these limitations. NN techniques are broadly classified into structure less and structure based techniques. In this paper, we present the survey of such techniques. Weighted kNN, Model based kNN, Condensed NN, Reduced NN, Generalized NN are structure less techniques whereas k-d tree, ball tree, Principal Axis Tree, Nearest Feature Line, Tunable NN, Orthogonal Search Tree are structure based algorithms developed on the basis of kNN. The structure less method overcome memory limitation and structure based techniques reduce the computational complexity.