A learning framework for nearest neighbor search

Can we leverage learning techniques to build a fast nearest-neighbor (ANN) retrieval data structure? We present a general learning framework for the NN problem in which sample queries are used to learn the parameters of a data structure that minimize the retrieval time and/or the miss rate. We explore the potential of this novel framework through two popular NN data structures: KD-trees and the rectilinear structures employed by locality sensitive hashing. We derive a generalization theory for these data structure classes and present simple learning algorithms for both. Experimental results reveal that learning often improves on the already strong performance of these data structures.

• Type: Conference
• Year: 2007
• Where: NIPS
• Authors: Lawrence Cayton, Sanjoy Dasgupta
• Keywords: Data Structures, General Learning Framework, Information Technology, NIPS 2007, Retrieval Data Structure