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SDM
2007
SIAM
126views Data Mining» more  SDM 2007»
13 years 8 months ago
Nonlinear 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-d...
Erion Plaku, Lydia E. Kavraki
PODS
1997
ACM
182views Database» more  PODS 1997»
13 years 11 months ago
A Cost Model For Nearest Neighbor Search in High-Dimensional Data Space
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...
Stefan Berchtold, Christian Böhm, Daniel A. K...
ALENEX
2001
215views Algorithms» more  ALENEX 2001»
13 years 8 months ago
An Empirical Study of a New Approach to Nearest Neighbor Searching
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...
Songrit Maneewongvatana, David M. Mount
EDBT
2006
ACM
154views Database» more  EDBT 2006»
13 years 11 months ago
Approximation Techniques to Enable Dimensionality Reduction for Voronoi-Based Nearest Neighbor Search
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
JMLR
2010
119views more  JMLR 2010»
13 years 2 months ago
Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data
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...
Milos Radovanovic, Alexandros Nanopoulos, Mirjana ...