A Highly Efficient Algorithm for Solving Exclusive Lasso Problems
The exclusive lasso (also known as elitist lasso) regularizer has become popular recently due to its superior performance on intra-group feature selection. Its complex nature poses difficulties for the computation of high-dimensional machine learning models involving such a regularizer. In this pape...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
25.06.2023
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Subjects | |
Online Access | Get full text |
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Summary: | The exclusive lasso (also known as elitist lasso) regularizer has become
popular recently due to its superior performance on intra-group feature
selection. Its complex nature poses difficulties for the computation of
high-dimensional machine learning models involving such a regularizer. In this
paper, we propose a highly efficient dual Newton method based proximal point
algorithm (PPDNA) for solving large-scale exclusive lasso models. As important
ingredients, we systematically study the proximal mapping of the weighted
exclusive lasso regularizer and the corresponding generalized Jacobian. These
results also make popular first-order algorithms for solving exclusive lasso
models more practical. Extensive numerical results are presented to demonstrate
the superior performance of the PPDNA against other popular numerical
algorithms for solving the exclusive lasso problems. |
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DOI: | 10.48550/arxiv.2306.14196 |