Proximal gradient methods for learning information
Proximal gradient (forward backward splitting) methods for learning is an area of research in optimization and statistical learning theory which studies algorithms for a general class of convex regularization problems where the regularization penalty may not be differentiable. One such example is regularization (also known as Lasso) of the form
Proximal gradient methods offer a general framework for solving regularization problems from statistical learning theory with penalties that are tailored to a specific problem application.[1][2] Such customized penalties can help to induce certain structure in problem solutions, such as sparsity (in the case of lasso) or group structure (in the case of group lasso).
^Combettes, Patrick L.; Wajs, Valérie R. (2005). "Signal Recovering by Proximal Forward-Backward Splitting". Multiscale Model. Simul. 4 (4): 1168–1200. doi:10.1137/050626090. S2CID 15064954.
^Mosci, S.; Rosasco, L.; Matteo, S.; Verri, A.; Villa, S. (2010). "Solving Structured Sparsity Regularization with Proximal Methods". Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science. Vol. 6322. pp. 418–433. doi:10.1007/978-3-642-15883-4_27. ISBN 978-3-642-15882-7.
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Proximalgradient (forward backward splitting) methodsforlearning is an area of research in optimization and statistical learning theory which studies...
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class of methods, and an area of research in statistical learning theory, that extend and generalize sparsity regularization learningmethods. Both sparsity...
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