ANN: Approximate Nearest Neighbors（近似最近邻分类算法）下载

weixin_39820780 2020-02-15 08:30:20

由国外著名大学编写的非常有效近似最近邻分类算法，可直接使用，也可作为学习
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由国外著名大学编写的非常有效近似最近邻分类算法，可直接使用，也可作为学习

Flann库 FLANN库全称是Fast Library for Approximate Nearest Neighbors,它是目前最完整的(近似)最近邻开源库。

Embedding的近邻搜索是当前图推荐系统非常重要的一种召回方式，通过item2vec、矩阵分解、双塔DNN等方式都能够产出训练好的user embedding、item embedding，对于embedding的使用非常的灵活：输入user embedding，近邻搜索item embedding，可以给user推荐感兴趣的items 输入user embedding，近邻搜搜user embedding，可以给user推荐感兴趣的user 输入item embedding，近邻搜索item embedding，可以给item推荐相关的items 然而有一个工程问题，一旦u

For many computer vision problems, the most time consuming component consists of nearest neighbor matching in high-dimensional spaces. There are no known exact algorithms for solving these high-dimensional problems that are faster than linear search. Approximate algorithms are known to provide large speedups with only minor loss in accuracy, but many such algorithms have been published with only minimal guidance on selecting an algorithm and its parameters for any given problem. In this paper, we describe a system that answers the question, “What is the fastest approximate nearest-neighbor algorithm for my data? ” Our system will take any given dataset and desired degree of precision and use these to automatically determine the best algorithm and parameter values. We also describe a new algorithm that applies priority search on hierarchical k-means trees, which we have found to provide the best known performance on many datasets. After testing a range of alternatives, we have found that multiple randomized k-d trees provide the best performance for other datasets. We are releasing public domain code that implements these approaches. This library provides about one order of magnitude improvement in query time over the best previously available software and provides fully automated parameter selection.

FLANN 库全称是Fast Library for Approximate Nearest Neighbors，它是目前最完整的（近似）最近邻开源库。不但实现了一系列查找算法，还包含了一种自动选取最快算法的机制。 FLANN 用 C 编写，包含以下语言的绑定：C、MATLAB、Python 和 Ruby 。标签：FLANN

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