On Locality-sensitive Indexing in Generic Metric Spaces
| Authors | |
|---|---|
| Year of publication | 2010 |
| Type | Article in Proceedings |
| Conference | 3rd International Conference on Similarity Search and Applications |
| MU Faculty or unit | |
| Citation | |
| Field | Informatics |
| Keywords | locality-sensitive hashing; metric space; similarity search; approximation; scalability |
| Description | The concept of Locality-sensitive Hashing (LSH) has been successfully used for searching in high-dimensional data and a number of locality-preserving hash functions have been introduced. In order to extend the applicability of the LSH approach to a general metric space, we focus on a recently presented Metric Index (M-Index), we redefine its hashing and searching process in the terms of LSH, and perform extensive measurements on two datasets to verify that the M-Index fulfills the conditions of the LSH concept. We widely discuss "optimal" properties of LSH functions and the efficiency of a given LSH function with respect to kNN queries. The results also indicate that the M-Index hashing and searching is more efficient than the tested standard LSH approach for Euclidean distance. |
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