SparseNet: Coordinate Descent With Nonconvex Penalties

We address the problem of sparse selection in linear models. A number of nonconvex penalties have been proposed in the literature for this purpose, along with a variety of convex-relaxation algorithms for finding good solutions. In this article we pursue a coordinate-descent approach for optimizatio...

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Veröffentlicht in:	Journal of the American Statistical Association 2011-09, Vol.106 (495), p.1125-1138
Hauptverfasser:	Mazumder, Rahul, Friedman, Jerome H., Hastie, Trevor
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Applications Calculus of variations and optimal control Calibration Computational methods Convergence Coordinate systems Degrees of freedom Error rates Estimators Exact sciences and technology Experiments Fines & penalties General topics LASSO Linear models Linear regression Mathematical analysis Mathematical minima Mathematical models Mathematics Methodology Modeling Nonconvex optimization Numerical analysis Numerical analysis. Scientific computation Numerical methods in mathematical programming, optimization and calculus of variations Numerical methods in optimization and calculus of variations Optimization algorithms Probability and statistics Regularization surface Sciences and techniques of general use Sparse regression Statistics Theory and Methods Threshing Variable coefficients Variable selection Vector-autoregressive models
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