Least square regression with lp-coefficient regularization

Hongzhi Tong; Di Rong Chen; Fenghong Yang

doi:10.1162/NECO_a_00044

Least square regression with l^p-coefficient regularization

Hongzhi Tong^*
, Di Rong Chen
, Fenghong Yang

^*Corresponding author for this work

School of Mathematical Sciences

University of International Business and Economics
Central University of Finance and Economics

Research output: Contribution to journal › Letter › peer-review

20 Scopus citations

Abstract

The selection of the penalty functional is critical for the performance of a regularized learning algorithm, and thus it deserves special attention. In this article, we present a least square regression algorithm based on l^p-coefficient regularization. Comparing with the classical regularized least square regression, the new algorithm is different in the regularization term. Our primary focus is on the error analysis of the algorithm. An explicit learning rate is derived under some ordinary assumptions.

Original language	English
Pages (from-to)	3221-3235
Number of pages	15
Journal	Neural Computation
Volume	22
Issue number	12
DOIs	https://doi.org/10.1162/NECO_a_00044
State	Published - Dec 2010

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title = "Least square regression with lp-coefficient regularization",

abstract = "The selection of the penalty functional is critical for the performance of a regularized learning algorithm, and thus it deserves special attention. In this article, we present a least square regression algorithm based on lp-coefficient regularization. Comparing with the classical regularized least square regression, the new algorithm is different in the regularization term. Our primary focus is on the error analysis of the algorithm. An explicit learning rate is derived under some ordinary assumptions.",

author = "Hongzhi Tong and Chen, \{Di Rong\} and Fenghong Yang",

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N2 - The selection of the penalty functional is critical for the performance of a regularized learning algorithm, and thus it deserves special attention. In this article, we present a least square regression algorithm based on lp-coefficient regularization. Comparing with the classical regularized least square regression, the new algorithm is different in the regularization term. Our primary focus is on the error analysis of the algorithm. An explicit learning rate is derived under some ordinary assumptions.

AB - The selection of the penalty functional is critical for the performance of a regularized learning algorithm, and thus it deserves special attention. In this article, we present a least square regression algorithm based on lp-coefficient regularization. Comparing with the classical regularized least square regression, the new algorithm is different in the regularization term. Our primary focus is on the error analysis of the algorithm. An explicit learning rate is derived under some ordinary assumptions.

UR - https://www.scopus.com/pages/publications/78649659487

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M3 - 快报

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Least square regression with l^p-coefficient regularization

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