Similarity search and locality sensitive hashing using ternary content addressable memories

Similarity search methods are widely used as kernels in various data mining and machine learning applications including those in computational biology, web search/clustering. Nearest neighbor search (NNS) algorithms are often used to retrieve similar entries, given a query. While there exist efficient techniques for exact query lookup using hashing, similarity search using exact nearest neighbors suffers from a ”curse of dimensionality”, i.e. for high dimensional spaces, best known solutions offer little improvement over brute force search and thus are unsuitable for large scale streaming applications. Fast solutions to the approximate NNS problem include Locality Sensitive Hashing (LSH) based techniques, which need storage polynomial in n with exponent greater than 1, and query time sublinear, but still polynomial in n, where n is the size of the database. In this work we present a new technique of solving the approximate NNS problem in Euclidean space using a Ternary Content A...

• Type: Conference
• Year: 2010
• Where: SIGMOD
• Authors: Rajendra Shinde, Ashish Goel, Pankaj Gupta, Debojyoti Dutta
• Keywords: Approximate Nns Problem, Database, Nearest Neighbors, Query Time, SIGMOD 2010