| Autor: |
Gaudioso, M., Giallombardo, G., Hiriart-Urruty, J.-B. |
| Předmět: |
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| Zdroj: |
Optimization Methods & Software; Feb2024, Vol. 39 Issue 1, p84-101, 18p |
| Abstrakt: |
We tackle the sparsity constrained optimization problem by resorting to polyhedral k-norm as a valid tool to emulate the $ \ell _0 $ ℓ 0 -pseudo-norm. The main novelty of the approach is the use of the dual of the k-norm, which allows to obtain a formulation amenable for a relaxation that can be efficiently handled by block coordinate methods. The advantage of the approach is that it does not require the solution of difference-of-convex programmes, unlike other k-norm based methods available in the literature. In fact, our block coordinate approach requires, at each iteration, the solution of two convex programmes, one of which can be solved in $ O(n\log n) $ O (nlog n) time. We apply the method to feature selection within the framework of Support Vector Machine classification, and we report the results obtained on some benchmark test problems. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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