Utilizing Indexes for Approximate and On-Line Nearest Neighbor Queries

We explore using index structures for effective approximate and on-line nearest neighbor queries. While many index structures have showed to suffer from the dimensionality curse, we believe that indexes can still be useful in providing quick approximate solutions to the nearest neighbor queries. Moreover, the information provided by the indexes can provide certain bounds that can be invaluable for on-line nearest neighbor queries. This paper explores the idea of applying current R-tree based indexes to approximate and on-line nearest neighbors with bounds. We experiment with various heuristics and compare the trade-off between accuracy and efficiency. Our results are compared to locality sensitive hashing (LSH) and they show the effectiveness of the proposed scheme. We also provide guidelines on how this can be useful in a practical sense.