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  Analysis of Fixed-Point and Coordinate Descent Algorithms for Regularized Kernel Methods

Dinuzzo, F. (2011). Analysis of Fixed-Point and Coordinate Descent Algorithms for Regularized Kernel Methods. IEEE Transactions on Neural Networks, 22(10), 1576-1587. doi:10.1109/TNN.2011.2164096.

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Dinuzzo, F1, Author              
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1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, DE, ou_1497647              

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 Abstract: In this paper, we analyze the convergence of two general classes of optimization algorithms for regularized kernel methods with convex loss function and quadratic norm regularization. The first methodology is a new class of algorithms based on fixed-point iterations that are well-suited for a parallel implementation and can be used with any convex loss function. The second methodology is based on coordinate descent, and generalizes some techniques previously proposed for linear support vector machines. It exploits the structure of additively separable loss functions to compute solutions of line searches in closed form. The two methodologies are both very easy to implement. In this paper, we also show how to remove non-differentiability of the objective functional by exactly reformulating a convex regularization problem as an unconstrained differentiable stabilization problem.

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 Dates: 2011-10
 Publication Status: Published in print
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 Identifiers: DOI: 10.1109/TNN.2011.2164096
BibTex Citekey: Dinuzzo2011_2
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Title: IEEE Transactions on Neural Networks
Source Genre: Journal
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Publ. Info: New York, NY : Institute of Electrical and Electronics Engineers
Pages: - Volume / Issue: 22 (10) Sequence Number: - Start / End Page: 1576 - 1587 Identifier: ISSN: 1045-9227
CoNE: https://pure.mpg.de/cone/journals/resource/954925591430