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SDM
2011
SIAM
414views Data Mining» more  SDM 2011»
12 years 10 months ago
Clustered low rank approximation of graphs in information science applications
In this paper we present a fast and accurate procedure called clustered low rank matrix approximation for massive graphs. The procedure involves a fast clustering of the graph and...
Berkant Savas, Inderjit S. Dhillon
EUROGP
2010
Springer
166views Optimization» more  EUROGP 2010»
14 years 22 days ago
Learning a Lot from Only a Little: Genetic Programming for Panel Segmentation on Sparse Sensory Evaluation Data
We describe a data mining framework that derives panelist information from sparse flavour survey data. One component of the framework executes genetic programming ensemble based s...
Katya Vladislavleva, Kalyan Veeramachaneni, Una-Ma...
MICCAI
2009
Springer
14 years 8 months ago
Functional Segmentation of fMRI Data Using Adaptive Non-negative Sparse PCA (ANSPCA)
We propose a novel method for functional segmentation of fMRI data that incorporates multiple functional attributes such as activation effects and functional connectivity, under a ...
Bernard Ng, Rafeef Abugharbieh, Martin J. McKeow...
BMCBI
2010
182views more  BMCBI 2010»
13 years 7 months ago
L2-norm multiple kernel learning and its application to biomedical data fusion
Background: This paper introduces the notion of optimizing different norms in the dual problem of support vector machines with multiple kernels. The selection of norms yields diff...
Shi Yu, Tillmann Falck, Anneleen Daemen, Lé...
CISS
2008
IEEE
14 years 2 months ago
Information theory based estimator of the number of sources in a sparse linear mixing model
—In this paper we present an Information Theoretic Estimator for the number of sources mutually disjoint in a linear mixing model. The approach follows the Minimum Description Le...
Radu Balan