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» EM in High Dimensional Spaces
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SIGMOD
1998
ACM
117views Database» more  SIGMOD 1998»
15 years 10 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 11 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
16 years 3 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
213
Voted
EDBT
2006
ACM
154views Database» more  EDBT 2006»
15 years 9 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»
15 years 6 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