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Many binary code embedding techniques have been proposed for large-scale approximate nearest neighbor search in computer vision. Recently, product quantization that encodes the cluster index in each subspace has been shown to provide impressive accuracy for nearest neighbor search. In this paper, we explore a simple question: is it best to use all the bit budget for encoding a cluster index in each subspace? We have found that as data points are located farther away from the centers of theirdoi:10.1109/cvpr.2014.274 dblp:conf/cvpr/HeoLY14 fatcat:244cpi4tbfephlsfkxbfjzxvte