Varying similarity metrics in indexing and retrieving high dimensional data

许多应用程序需要通过对不同维度赋予权重来改变距离度量。以视觉信息检索为例，由于相似性度量的主观性质，不同用户之间的距离度量可能会有所不同。在高维空间中使用加权度量的k近邻（k-nn）搜索的主要问题是搜索是否可以有效地完成，因为在任意距离度量中，k-nn球体和索引树中节点的边界包络之间的交集不能容易地计算。该项目旨在寻求一种新的解决方案。它将在高维视觉信息检索、基于实例的学习、复分析、非参数密度估计等领域产生巨大的影响和应用。

Many applications require varying distance metrics by putting a weight on different dimensions. Taking an example in visual information retrieval, due to the subjective nature of similarity measurements, the distance metrics can vary between different users. The main problem with k nearest neighbour (k-nn) searches using weighted metrics in a high dimensional space is whether the searches can be done efficiently, since in an arbitrary distance metric the intersection between the k-nn sphere and the bounding envelopes of nodes in an index tree cannot be easily calculated. This project aims to seek a novel solution. It will create a great impact on and have many applications in high dimensional visual information retrieval, instance-based learning, complex analysis, non-parametric density estimation, etc.