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» On High Dimensional Skylines
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CIKM
2003
Springer
14 years 3 months ago
Dimensionality reduction using magnitude and shape approximations
High dimensional data sets are encountered in many modern database applications. The usual approach is to construct a summary of the data set through a lossy compression technique...
Ümit Y. Ogras, Hakan Ferhatosmanoglu
CVPR
2008
IEEE
14 years 12 months ago
Dimensionality reduction by unsupervised regression
We consider the problem of dimensionality reduction, where given high-dimensional data we want to estimate two mappings: from high to low dimension (dimensionality reduction) and f...
Miguel Á. Carreira-Perpiñán, ...
ALENEX
2001
105views Algorithms» more  ALENEX 2001»
13 years 11 months ago
A Probabilistic Spell for the Curse of Dimensionality
Range searches in metric spaces can be very di cult if the space is \high dimensional", i.e. when the histogram of distances has a large mean and a small variance. The so-cal...
Edgar Chávez, Gonzalo Navarro
ICPR
2000
IEEE
14 years 2 months ago
Two-Stage Computational Cost Reduction Algorithm Based on Mahalanobis Distance Approximations
For many pattern recognition methods, high recognition accuracy is obtained at very high expense of computational cost. In this paper, a new algorithm that reduces the computation...
Fang Sun, Shinichiro Omachi, Nei Kato, Hirotomo As...
EDBT
2006
ACM
154views Database» more  EDBT 2006»
14 years 1 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