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» Outlier Detection for High Dimensional Data
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BMCBI
2005
112views more  BMCBI 2005»
14 years 9 months ago
Visualization methods for statistical analysis of microarray clusters
Background: The most common method of identifying groups of functionally related genes in microarray data is to apply a clustering algorithm. However, it is impossible to determin...
Matthew A. Hibbs, Nathaniel C. Dirksen, Kai Li, Ol...
ACCV
2009
Springer
15 years 4 months ago
A Dynamic Programming Approach to Maximizing Tracks for Structure from Motion
We present a novel algorithm for improving the accuracy of structure from motion on video sequences. Its goal is to efficiently recover scene structure and camera pose by using dyn...
Jonathan Mooser, Suya You, Ulrich Neumann, Raphael...
CGF
2011
14 years 1 months ago
Visualizing High-Dimensional Structures by Dimension Ordering and Filtering using Subspace Analysis
High-dimensional data visualization is receiving increasing interest because of the growing abundance of highdimensional datasets. To understand such datasets, visualization of th...
Bilkis J. Ferdosi, Jos B. T. M. Roerdink
ISCA
2007
IEEE
217views Hardware» more  ISCA 2007»
14 years 9 months ago
Parallel Processing of High-Dimensional Remote Sensing Images Using Cluster Computer Architectures
Hyperspectral sensors represent the most advanced instruments currently available for remote sensing of the Earth. The high spatial and spectral resolution of the images supplied ...
David Valencia, Antonio Plaza, Pablo Martín...
171
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ICDE
2007
IEEE
165views Database» more  ICDE 2007»
15 years 10 months ago
On Randomization, Public Information and the Curse of Dimensionality
A key method for privacy preserving data mining is that of randomization. Unlike k-anonymity, this technique does not include public information in the underlying assumptions. In ...
Charu C. Aggarwal