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Proceedings Paper

An improved sparse LS-SVR for estimating illumination
Author(s): Zhenmin Zhu; Zhaokang Lv; Baifen Liu
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Paper Abstract

Support Vector Regression performs well on estimating illumination chromaticity in a scene. Then the concept of Least Squares Support Vector Regression has been put forward as an effective, statistical and learning prediction model. Although it is successful to solve some problems of estimation, it also has obvious defects. Due to a large amount of support vectors which are chosen in the process of training LS-SVR , the calculation become very complex and it lost the sparsity of SVR. In this paper, we get inspiration from WLS-SVM(Weighted Least Squares Support Vector Machines) and a new method for sparse model. A Density Weighted Pruning algorithm is used to improve the sparsity of LS-SVR and named SLS-SVR(Sparse Least Squares Support Vector Regression).The simulation indicates that only need to select 30 percent of support vectors, the prediction can reach to 75 percent of the original one.

Paper Details

Date Published: 6 July 2015
PDF: 6 pages
Proc. SPIE 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015), 963107 (6 July 2015); doi: 10.1117/12.2197082
Show Author Affiliations
Zhenmin Zhu, East China Jiao Tong Univ. (China)
Zhaokang Lv, East China Jiao Tong Univ. (China)
Baifen Liu, East China Jiao Tong Univ. (China)


Published in SPIE Proceedings Vol. 9631:
Seventh International Conference on Digital Image Processing (ICDIP 2015)
Charles M. Falco; Xudong Jiang, Editor(s)

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