使用仲裁非欧几里德度量的本地敏感哈希 [英] Local Sensitive Hashing using a arbitray non euclidean metric

问题描述

我有一个非常具体的问题。我在一个项目上工作，我需要找到最近的邻居（k和附近）。
由于我不需要这些，希望能够扩展到高维度，我专注于LSH。

我的数据有一个距离公制，但非欧几里德。我发现使用欧几里德度量（例如p稳定分布），二进制编码（通过投影）或基于字符串的向量空间的许多方法。

我正在搜索的是提供任意指标的LSH模板的论文。有没有人有一些参考文献？

提前感谢
Dan

解决方案

你正在寻找的是相当新的：
我认为这篇文章可能会帮助
http://www.aaai.org/ocs/index.php/aaai/aaai10/paper/download/1839/2032

它提出非度量数据的策略，
比非欧几里亚的情况更糟糕。

I have a very specific question. I work on a project, were I need to find nearest neighbours (k and near). As I dont need the excat ones and want to be able to extend to high dimensions, I focused on LSH.

My data has a distance that is a metric, but non euclidean. I found many ways for vector space with euclidean metric (e.g. the p stable distribution), binary coding(via projections) or string based.

What I am searching are papers that present a LSH template for an arbitrary metric. Does anyone has some refernece to papers?

Thanks in advance Dan

解决方案

What you are looking for is quite new: I think this paper may help http://www.aaai.org/ocs/index.php/aaai/aaai10/paper/download/1839/2032

It suggests strategies for non-metric data, which is even worse than having a non-euclidean case.

这篇关于使用仲裁非欧几里德度量的本地敏感哈希的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！