Search Machine Learning Repository: @inproceedings{icml2014c2_zhangd14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {Composite Quantization for Approximate Nearest Neighbor Search},
    Url = {http://jmlr.org/proceedings/papers/v32/zhangd14.pdf},
    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.},
    Author = {Ting Zhang and Chao Du and Jingdong Wang},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {838-846}
   }