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Product quantization (PQ) is a popular vector quantization method for approximate nearest neighbor search. The key idea of PQ is to decompose the original data space into the Cartesian product of some low-dimensional subspaces and then every subspace is quantized separately with the same number of codewords. However, the performance of PQ depends largely on the distribution of the original data. If...
Using hashing algorithms to learn binary codes representation of data for fast approximate nearest neighbor (ANN) search has attracted more and more attention. Most existing hashing methods employ various hash functions to encode data. The resulting binary codes can be obtained by concatenating bits produced by those hash functions. These methods usually have two main steps: projection and thresholding...
Using hashing algorithms to learn binary codes representation of data for fast approximate nearest neighbor (ANN) search has attracted more and more attentions. Most existing hashing methods employ various hash functions to encode data. The resulting binary codes can be obtained by concatenating bits produced by those hash functions. These methods usually have two main steps: projection and thresholding...
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