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» Dimensionality Reduction of Clustered Data Sets
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SODA
2010
ACM
171views Algorithms» more  SODA 2010»
16 years 3 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...
190
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JMLR
2010
150views more  JMLR 2010»
15 years 27 days ago
Supervised Dimension Reduction Using Bayesian Mixture Modeling
We develop a Bayesian framework for supervised dimension reduction using a flexible nonparametric Bayesian mixture modeling approach. Our method retrieves the dimension reduction ...
Kai Mao, Feng Liang, Sayan Mukherjee
ICASSP
2010
IEEE
15 years 6 months ago
Swift: Scalable weighted iterative sampling for flow cytometry clustering
Flow cytometry (FC) is a powerful technology for rapid multivariate analysis and functional discrimination of cells. Current FC platforms generate large, high-dimensional datasets...
Iftekhar Naim, Suprakash Datta, Gaurav Sharma, Jam...
KDD
2006
ACM
145views Data Mining» more  KDD 2006»
16 years 6 months ago
Deriving quantitative models for correlation clusters
Correlation clustering aims at grouping the data set into correlation clusters such that the objects in the same cluster exhibit a certain density and are all associated to a comm...
Arthur Zimek, Christian Böhm, Elke Achtert, H...
MICAI
2005
Springer
15 years 11 months ago
Proximity Searching in High Dimensional Spaces with a Proximity Preserving Order
Abstract. Kernel based methods (such as k-nearest neighbors classifiers) for AI tasks translate the classification problem into a proximity search problem, in a space that is usu...
Edgar Chávez, Karina Figueroa, Gonzalo Nava...