Similarity Search in High Dimensions via Hashing

The nearest- or near-neighbor query problems arise in a large variety of database applications, usually in the context of similarity searching. Of late, there has been increasing interest in building search index structures for performing similarity search over high-dimensional data, e.g., image databases, document collections, time-series databases, and genome databases. Unfortunately, all known techniques for solving this problem fall prey to the curse of dimensionality." That is, the data structures scale poorly with data dimensionality; in fact, if the number of dimensions exceeds 10 to 20, searching in k-d trees and related structures involves the inspection of a large fraction of the database, thereby doing no better than brute-force linear search. It has been suggested that since the selection of features and the choice of a distance metric in typical applications is rather heuristic, determining an approximate nearest neighbor should su ce for most practical purposes. In ...

• Type: Conference
• Year: 1999
• Where: VLDB
• Authors: Aristides Gionis, Piotr Indyk, Rajeev Motwani
• Keywords: Approximate Similarity Search, Database, Grant IIS-9811904, Similarity Search, VLDB 1999