TY - JOUR

T1 - Bi-level path following for cross validated solution of kernel quantile regression

AU - Rosset, Saharon

PY - 2009

Y1 - 2009

N2 - We show how to follow the path of cross validated solutions to families of regularized optimization problems, defined by a combination of a parameterized loss function and a regularization term. A primary example is kernel quantile regression, where the parameter of the loss function is the quantile being estimated. Even though the bi-level optimization problem we encounter for every quantile is non-convex, the manner in which the optimal cross-validated solution evolves with the parameter of the loss function allows tracking of this solution. We prove this property, construct the resulting algorithm, and demonstrate it on real and artificial data. This algorithm allows us to efficiently solve the whole family of bi-level problems. We show how it can be extended to cover other modeling problems, like support vector regression, and alternative in-sample model selection approaches.1

AB - We show how to follow the path of cross validated solutions to families of regularized optimization problems, defined by a combination of a parameterized loss function and a regularization term. A primary example is kernel quantile regression, where the parameter of the loss function is the quantile being estimated. Even though the bi-level optimization problem we encounter for every quantile is non-convex, the manner in which the optimal cross-validated solution evolves with the parameter of the loss function allows tracking of this solution. We prove this property, construct the resulting algorithm, and demonstrate it on real and artificial data. This algorithm allows us to efficiently solve the whole family of bi-level problems. We show how it can be extended to cover other modeling problems, like support vector regression, and alternative in-sample model selection approaches.1

UR - http://www.scopus.com/inward/record.url?scp=73549100890&partnerID=8YFLogxK

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AN - SCOPUS:73549100890

SN - 1532-4435

VL - 10

SP - 2473

EP - 2505

JO - Journal of Machine Learning Research

JF - Journal of Machine Learning Research

ER -