Search Machine Learning Repository:
Composite Quantization for Approximate Nearest Neighbor Search
Authors: Ting Zhang, Chao Du and Jingdong Wang
Conference: Proceedings of the 31st International Conference on Machine Learning (ICML-14)
Abstract: This paper presents a novel compact coding approach, composite quantization, for approximate nearest neighbor search. The idea is to use the composition of several elements selected from the dictionaries to accurately approximate a vector and to represent the vector by a short code composed of the indices of the selected elements. To efficiently compute the approximate distance of a query to a database vector using the short code, we introduce an extra constraint, constant inter-dictionary-element-product, resulting in that approximating the distance only using the distance of the query to each selected element is enough for nearest neighbor search. Experimental comparison with state-of-the-art algorithms over several benchmark datasets demonstrates the efficacy of the proposed approach.
authors venues years
Suggest Changes to this paper.
Brought to you by the WUSTL Machine Learning Group. We have open faculty positions (tenured and tenure-track).