Comparison of l₁-Norm SVR and Sparse Coding Algorithms for Linear Regression

Support vector regression (SVR) is a popular function estimation technique based on Vapnik's concept of support vector machine. Among many variants, the l1-norm SVR is known to be good at selecting useful features when the features are redundant. Sparse coding (SC) is a technique widely used in...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 26; no. 8; p. 1828
Main Authors: Zhang, Qingtian, Hu, Xiaolin, Zhang, Bo
Format: Journal Article
Language:English
Published: United States 01.08.2015
Subjects:
Algorithms
Linear Models
Neural Networks (Computer)
Support Vector Machine
Time Factors
ISSN:2162-2388, 2162-2388
Online Access:Get full text
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Summary:Support vector regression (SVR) is a popular function estimation technique based on Vapnik's concept of support vector machine. Among many variants, the l1-norm SVR is known to be good at selecting useful features when the features are redundant. Sparse coding (SC) is a technique widely used in many areas and a number of efficient algorithms are available. Both l1-norm SVR and SC can be used for linear regression. In this brief, the close connection between the l1-norm SVR and SC is revealed and some typical algorithms are compared for linear regression. The results show that the SC algorithms outperform the Newton linear programming algorithm, an efficient l1-norm SVR algorithm, in efficiency. The algorithms are then used to design the radial basis function (RBF) neural networks. Experiments on some benchmark data sets demonstrate the high efficiency of the SC algorithms. In particular, one of the SC algorithms, the orthogonal matching pursuit is two orders of magnitude faster than a well-known RBF network designing algorithm, the orthogonal least squares algorithm.
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ISSN:2162-2388
2162-2388
DOI:10.1109/TNNLS.2014.2377245