Nearest neighbors search using point location in balls with applications to approximate Voronoi decompositions
نویسندگان
چکیده
منابع مشابه
Chapter 14 Approximate Nearest Neighbor via Point - Location among Balls
x Today I know that everything watches, that nothing goes unseen, and that even wallpaper has a better memory than ours. It isn't God in His heaven that sees all. A kitchen chair, a coat-hanger a half-filled ash tray, or the wood replica of a woman name Niobe, can perfectly well serve as an unforgetting witness to every one of our acts. – The tin drum, Gunter Grass 14.1 Hierarchical Representat...
متن کامل(Approximate) Conic Nearest Neighbors and the induced Voronoi Diagram
For a given point set in Euclidean space we consider the problem of finding (approximate) nearest neighbors of a query point but restricting only to points that lie within a fixed cone with apex at the query point. Apart from being a rather natural question to ask, solutions to this problem have applications in surface reconstruction and dimension detection. We investigate the structure of the ...
متن کاملNonlinear Dimensionality Reduction using Approximate Nearest Neighbors
Nonlinear dimensionality reduction methods often rely on the nearest-neighbors graph to extract low-dimensional embeddings that reliably capture the underlying structure of high-dimensional data. Research however has shown that computing nearest neighbors of a point from a highdimensional data set generally requires time proportional to the size of the data set itself, rendering the computation...
متن کاملRandomized approximate nearest neighbors algorithm.
We present a randomized algorithm for the approximate nearest neighbor problem in d-dimensional Euclidean space. Given N points {x(j)} in R(d), the algorithm attempts to find k nearest neighbors for each of x(j), where k is a user-specified integer parameter. The algorithm is iterative, and its running time requirements are proportional to T·N·(d·(log d) + k·(d + log k)·(log N)) + N·k(2)·(d + l...
متن کاملApproximate K Nearest Neighbors in High Dimensions
Given a set P of N points in a ddimensional space, along with a query point q, it is often desirable to find k points of P that are with high probability close to q. This is the Approximate k-NearestNeighbors problem. We present two algorithms for AkNN. Both require O(Nd) preprocessing time. The first algorithm has a query time cost that is O(d+logN), while the second has a query time cost that...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Computer and System Sciences
سال: 2006
ISSN: 0022-0000
DOI: 10.1016/j.jcss.2006.01.007