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» The Iso-regularization Descent Algorithm for the LASSO
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CORR
2011
Springer
205views Education» more  CORR 2011»
12 years 8 months ago
Parallel Coordinate Descent for L1-Regularized Loss Minimization
We propose Shotgun, a parallel coordinate descent algorithm for minimizing L1regularized losses. Though coordinate descent seems inherently sequential, we prove convergence bounds...
Joseph K. Bradley, Aapo Kyrola, Danny Bickson, Car...
ICML
2010
IEEE
13 years 5 months ago
Simple and Efficient Multiple Kernel Learning by Group Lasso
We consider the problem of how to improve the efficiency of Multiple Kernel Learning (MKL). In literature, MKL is often solved by an alternating approach: (1) the minimization of ...
Zenglin Xu, Rong Jin, Haiqin Yang, Irwin King, Mic...
JMLR
2010
110views more  JMLR 2010»
12 years 11 months ago
Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net
A new approach to regression regularization called the Pairwise Elastic Net is proposed. Like the Elastic Net, it simultaneously performs automatic variable selection and continuo...
Alexander Lorbert, David Eis, Victoria Kostina, Da...
CVPR
2010
IEEE
14 years 2 months ago
Online Visual Vocabulary Pruning Using Pairwise Constraints
Given a pair of images represented using bag-of-visual words and a label corresponding to whether the images are “related”(must-link constraint) or “unrelated” (must not li...
Pavan Mallapragada, Rong Jin and Anil Jain
ICML
2009
IEEE
13 years 2 months ago
Multiple indefinite kernel learning with mixed norm regularization
We address the problem of learning classifiers using several kernel functions. On the contrary to many contributions in the field of learning from different sources of information...
Matthieu Kowalski, Marie Szafranski, Liva Ralaivol...