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» EM in High Dimensional Spaces
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SIGMOD
1998
ACM
117views Database» more  SIGMOD 1998»
15 years 2 months ago
The Pyramid-Technique: Towards Breaking the Curse of Dimensionality
In this paper, we propose the Pyramid-Technique, a new indexing method for high-dimensional data spaces. The PyramidTechnique is highly adapted to range query processing using the...
Stefan Berchtold, Christian Böhm, Hans-Peter ...
ICCV
2009
IEEE
16 years 3 months ago
Dimensionality Reduction and Principal Surfaces via Kernel Map Manifolds
We present a manifold learning approach to dimensionality reduction that explicitly models the manifold as a mapping from low to high dimensional space. The manifold is represen...
Samuel Gerber, Tolga Tasdizen, Ross Whitaker
ALT
2004
Springer
15 years 7 months ago
On Kernels, Margins, and Low-Dimensional Mappings
Kernel functions are typically viewed as providing an implicit mapping of points into a high-dimensional space, with the ability to gain much of the power of that space without inc...
Maria-Florina Balcan, Avrim Blum, Santosh Vempala
EDBT
2006
ACM
154views Database» more  EDBT 2006»
15 years 2 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
JEA
2008
112views more  JEA 2008»
14 years 10 months ago
Dynamic spatial approximation trees
The Spatial Approximation Tree (sa-tree) is a recently proposed data structure for searching in metric spaces. It has been shown that it compares favorably against alternative data...
Gonzalo Navarro, Nora Reyes