基于Quantization的快速内积搜索
Quantization based Fast Inner Product Search
摘要：
我们提出了一种基于量化的快速近似最大内积搜索（MIPS）方法。每个数据库向量通过一组码本在多个子空间中量化，直接通过最小化内积量化误差来学习。然后，查询到数据库向量的内积近似为与子空间量化器的内积之和。与最近提出的MIPS LSH方法不同，数据库向量和查询不需要在高维特征空间中进行扩充。我们还提供了一个理论分析所提出的方法，包括温和的假设下的浓度结果。此外，如果一个小样本的示例查询是在训练时间，我们提出了一个修改的码本学习过程，进一步提高了准确性。在包括深度神经网络在内的各种数据集上的实验结果表明，所提出的方法显着优于现有的最先进的。
英文摘要：
We propose a quantization based approach for fast approximate Maximum Inner Product Search (MIPS). Each database vector is quantized in multiple subspaces via a set of codebooks, learned directly by minimizing the inner product quantization error. Then, the inner product of a query to a database vector is approximated as the sum of inner products with the subspace quantizers. Different from recently proposed LSH approaches to MIPS, the database vectors and queries do not need to be augmented in a higher dimensional feature space. We also provide a theoretical analysis of the proposed approach, consisting of the concentration results under mild assumptions. Furthermore, if a small sample of example queries is given at the training time, we propose a modified codebook learning procedure which further improves the accuracy. Experimental results on a variety of datasets including those arising from deep neural networks show that the proposed approach significantly outperforms the existing state-of-the-art.
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