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» Robust estimation for sparse data
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ICML
2009
IEEE
15 years 10 months ago
Group lasso with overlap and graph lasso
We propose a new penalty function which, when used as regularization for empirical risk minimization procedures, leads to sparse estimators. The support of the sparse vector is ty...
Laurent Jacob, Guillaume Obozinski, Jean-Philippe ...
BIOINFORMATICS
2006
106views more  BIOINFORMATICS 2006»
14 years 9 months ago
Identification of biochemical networks by S-tree based genetic programming
Motivation: Most previous approaches to model biochemical networks havefocusedeither on the characterization of a networkstructurewith a number of components or on the estimation ...
Dong-Yeon Cho, Kwang-Hyun Cho, Byoung-Tak Zhang
BILDMED
2006
169views Algorithms» more  BILDMED 2006»
14 years 11 months ago
Segmentation of the Vascular Tree in CT Data Using Implicit Active Contours
Abstract. We propose an algorithm for the segmentation of blood vessels in the kind of CT-data typical for diagnostics in a clinical environment. Due to poor quality and variance i...
Karsten Rink, Arne-Michael Törsel, Klaus D. T...
ICARCV
2002
IEEE
110views Robotics» more  ICARCV 2002»
15 years 2 months ago
A novel robust method for large numbers of gross errors
In computer vision tasks, it frequently happens that gross noise occupies the absolute majority of the data. Most robust estimators can tolerate no more than 50% gross errors. In ...
Hanzi Wang, David Suter
ISBI
2009
IEEE
15 years 4 months ago
3D Eigenfunction Expansion of Sparsely Sampled 2D Cortical Data
Various cortical measures such as cortical thickness are routinely computed along the vertices of cortical surface meshes. These metrics are used in surface-based morphometric stu...
Moo K. Chung, Yu-Chien Wu, Andrew L. Alexander