Nearest neighbor queries on extensible grid files using dimensionality reduction

Nowadays there have several applications on spatial information which manage high dimensional data. Whenever we examine nearest neighbor search in these applications by multi-dimensional indexing structure, very often we must access all pages if dimensionality exceeds about 10. This is known as curse of dimensionality that says any indexing structure is outperformed by simple linear search. In this investigation, for high dimensional data, we propose a sophisticated access mechanism based on extensible grid files with dimensionality reduction (DR) technique. We analyze error estimation caused by DR and recover the search space on original dimension. We examine nearest neighbor search and discuss some empirical results to show the usefulness of our approach.