Nonlinear dimensionality reduction methods often rely on the nearest-neighbors graph to extract low-dimensional embeddings that reliably capture the underlying structure of high-d...
In this paper, we present a new cost model for nearest neighbor search in high-dimensional data space. We first analyze different nearest neighbor algorithms, present a generaliza...
In nearest neighbor searching we are given a set of n data points in real d-dimensional space, d , and the problem is to preprocess these points into a data structure, so that give...
Utilizing spatial index structures on secondary memory for nearest neighbor search in high-dimensional data spaces has been the subject of much research. With the potential to host...
Christoph Brochhaus, Marc Wichterich, Thomas Seidl
Different aspects of the curse of dimensionality are known to present serious challenges to various machine-learning methods and tasks. This paper explores a new aspect of the dim...