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» On Approximating the Radii of Point Sets in High Dimensions
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PR
2007
100views more  PR 2007»
13 years 7 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
PSIVT
2007
Springer
110views Multimedia» more  PSIVT 2007»
14 years 1 months ago
Measuring Linearity of Ordered Point Sets
It is often practical to measure how linear a certain ordered set of points is. We are interested in linearity measures which are invariant to rotation, scaling, and translation. T...
Milos Stojmenovic, Amiya Nayak
PKDD
2010
Springer
169views Data Mining» more  PKDD 2010»
13 years 5 months ago
Classification with Sums of Separable Functions
Abstract. We present a novel approach for classification using a discretised function representation which is independent of the data locations. We construct the classifier as a su...
Jochen Garcke
FMSD
2006
104views more  FMSD 2006»
13 years 7 months ago
Some ways to reduce the space dimension in polyhedra computations
Convex polyhedra are often used to approximate sets of states of programs involving numerical variables. The manipulation of convex polyhedra relies on the so-called double descri...
Nicolas Halbwachs, David Merchat, Laure Gonnord
SIAMSC
2010
270views more  SIAMSC 2010»
13 years 6 months ago
High Order Numerical Methods to Three Dimensional Delta Function Integrals in Level Set Methods
In this paper we propose a class of high order numerical methods to delta function integrals appearing in level set methods in the three dimensional case by extending the idea for ...
Xin Wen