使用仲裁非欧几里德度量的本地敏感哈希

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

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 advanceDan

解决方案

What you are looking for is quite new:I think this paper may helphttp://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.

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