IEEE Trans Neural Netw Learn Syst - Comparison of l1-Norm SVR and Sparse Coding Algorithms for Linear Regression.

Tópicos

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Resumo

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.

Resumo Limpo

support vector regress svr popular function estim techniqu base vapnik concept support vector machin among mani variant lnorm svr known good select use featur featur redund spars code sc techniqu wide use mani area number effici algorithm avail lnorm svr sc can use linear regress brief close connect lnorm svr sc reveal typic algorithm compar linear regress result show sc algorithm outperform newton linear program algorithm effici lnorm svr algorithm effici algorithm use design radial basi function rbf neural network experi benchmark data set demonstr high effici sc algorithm particular one sc algorithm orthogon match pursuit two order magnitud faster wellknown rbf network design algorithm orthogon least squar algorithm

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