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Elastic-net constrained multiple kernel learning using a majorization-minimization approach

Citi, L (2015) Elastic-net constrained multiple kernel learning using a majorization-minimization approach. In: UNSPECIFIED, ? - ?.

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This papers introduces an algorithm for the solution of multiple kernel learning (MKL) problems with elastic-net constraints on the kernel weights. While efficient algorithms exist for MKL problems with L1-and Lp-norm (p > 1) constraints, a similar algorithm was lacking in the case of MKL under elastic-net constraints. For example, algorithms based on the cutting plane method require large and/or commercial libraries. The algorithm presented here can solve elastic-net constrained MKL problems very efficiently with simple code that does not rely on external libraries (except a conventional SVM solver). Based on majorization-minimization (MM), at each step it optimizes the kernel weights by minimizing a carefully designed surrogate function, called a majorizer, which can be solved in closed form. This improved efficiency and applicability facilitates the inclusion of elastic-net constrained MKL in existing open-source machine learning libraries.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2015 7th Computer Science and Electronic Engineering Conference, CEEC 2015 - Conference Proceedings
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 24 Nov 2015 14:11
Last Modified: 22 Jun 2021 19:15

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