We address the problem of how to compute the coefficient path implicitly defined by the differential geometric LARS (dgLARS) method in a high-dimensional setting. Although the geometrical theory developed to define the dgLARS method does not need of the definition of a penalty function, we show that it is possible to develop a cyclic coordinate descent algorithm to compute the solution curve in a high-dimensional setting. Simulation studies show that the proposed algorithm is significantly faster than the prediction-corrector algorithm originally developed to compute the dgLARS solution curve.
Augugliaro, L., Mineo, A., Wit, E. (2012). Differential geometric LARS via cyclic coordinate descent method. In Proceedings of COMPSTAT 2012, 20th International Conference on Computational Statistics (pp. 67-79). International Statistical Institute / International Association for Statistical Computing.
Differential geometric LARS via cyclic coordinate descent method
AUGUGLIARO, Luigi;MINEO, Angelo;
2012-01-01
Abstract
We address the problem of how to compute the coefficient path implicitly defined by the differential geometric LARS (dgLARS) method in a high-dimensional setting. Although the geometrical theory developed to define the dgLARS method does not need of the definition of a penalty function, we show that it is possible to develop a cyclic coordinate descent algorithm to compute the solution curve in a high-dimensional setting. Simulation studies show that the proposed algorithm is significantly faster than the prediction-corrector algorithm originally developed to compute the dgLARS solution curve.File | Dimensione | Formato | |
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