Abstract. Nearest neighbor search has a wide variety of applications. Unfortunately, the majority of search methods do not scale well with dimensionality. Recent efforts have been ...
We investigate the optimality of (1+ )-approximation algorithms obtained via the dimensionality reduction method. We show that: • Any data structure for the (1 + )-approximate n...
In this paper, we focus on the use of random projections as a dimensionality reduction tool for sampled manifolds in highdimensional Euclidean spaces. We show that geodesic paths ...
We investigate the problem of approximate similarity (nearest neighbor) search in high-dimensional metric spaces, and describe how the distance distribution of the query object ca...
Matching of high-dimensional features using nearest neighbors search is an important part of image matching methods which are based on local invariant features. In this work we hi...