Product quantization is now considered as an effective approach to solve the approximate nearest neighbor (ANN) search. A collection of derivative algorithms have been developed. However, the current techniques ignore the intrinsic high order structures of data, which usually contain helpful information for improving the computational precision. In this paper, aiming at the complex structure of high order data, we design an optimized technique, called optimized high order product quantization (O-HOPQ) for ANN search. In O-HOPQ, we incorporate the high order structures of the data into the process of designing a more effective subspace decomposition way. As a result, spatial adjacent elements in the high order data space are grouped into the same sub-space. Then, O-HOPQ generates its spatial structured code-book, by optimizing the quantization distortion. Starting from the structured codebook, the global optimum quantizers can be obtained effectively and efficiently. Experimental results show that appropriate utilization of the potential information that exists in the complex structure of high order data will result in significant improvements to the performance of the product quantizers. Besides, the high order structure based approaches are effective to the scenario where the data have intrinsic complex structures.

中文翻译：

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针对近似最近邻居搜索的优化高阶产品量化

现在，产品量化被认为是解决近似最近邻（ANN）搜索的有效方法。已经开发了一组衍生算法。但是，当前的技术忽略了数据的固有高阶结构，该结构通常包含有助于提高计算精度的有用信息。在本文中，针对高阶数据的复杂结构，我们设计了一种用于ANN搜索的优化技术，称为优化高阶乘积量化（O-HOPQ）。在O-HOPQ中，我们将数据的高阶结构合并到设计更有效的子空间分解方式的过程中。结果，高阶数据空间中的空间相邻元素被分组为相同的子空间。然后，O-HOPQ生成其空间结构化的代码本，通过优化量化失真。从结构化密码本开始，可以高效地获得全局最优量化器。实验结果表明，对存在于高阶数据复杂结构中的潜在信息的适当利用将显着提高产品量化器的性能。此外，基于高阶结构的方法对于数据具有固有的复杂结构的情况是有效的。