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BMCBI
2007
123views more  BMCBI 2007»
14 years 9 months ago
Robust clustering in high dimensional data using statistical depths
Background: Mean-based clustering algorithms such as bisecting k-means generally lack robustness. Although componentwise median is a more robust alternative, it can be a poor cent...
Yuanyuan Ding, Xin Dang, Hanxiang Peng, Dawn Wilki...
PR
2007
100views more  PR 2007»
14 years 9 months ago
Linear manifold clustering in high dimensional spaces by stochastic search
Classical clustering algorithms are based on the concept that a cluster center is a single point. Clusters which are not compact around a single point are not candidates for class...
Robert M. Haralick, Rave Harpaz
ICDE
2008
IEEE
158views Database» more  ICDE 2008»
15 years 11 months ago
CARE: Finding Local Linear Correlations in High Dimensional Data
Finding latent patterns in high dimensional data is an important research problem with numerous applications. Existing approaches can be summarized into 3 categories: feature selec...
Xiang Zhang, Feng Pan, Wei Wang
114
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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...
SODA
2010
ACM
171views Algorithms» more  SODA 2010»
15 years 7 months ago
Coresets and Sketches for High Dimensional Subspace Approximation Problems
We consider the problem of approximating a set P of n points in Rd by a j-dimensional subspace under the p measure, in which we wish to minimize the sum of p distances from each p...
Dan Feldman, Morteza Monemizadeh, Christian Sohler...