77 search results - page 1 / 16 » Gradient LASSO for feature selection |
ICML
2004
IEEE
13 years 10 months ago
2004
IEEE
LASSO (Least Absolute Shrinkage and Selection Operator) is a useful tool to achieve the shrinkage and variable selection simultaneously. Since LASSO uses the L1 penalty, the optim...
CORR
2010
Springer
13 years 4 months ago
2010
Springer
The group Lasso is an extension of the Lasso for feature selection on (predefined) non-overlapping groups of features. The non-overlapping group structure limits its applicability...
JMLR
2010
12 years 11 months ago
2010
We propose a novel group regularization which we call exclusive lasso. Unlike the group lasso regularizer that assumes covarying variables in groups, the proposed exclusive lasso ...
JMLR
2006
13 years 4 months ago
2006
Sparsity or parsimony of statistical models is crucial for their proper interpretations, as in sciences and social sciences. Model selection is a commonly used method to find such...
JMLR
2010
12 years 11 months ago
2010
L1 (also referred to as the 1-norm or Lasso) penalty based formulations have been shown to be effective in problem domains when noisy features are present. However, the L1 penalty...