Share Email Print

Journal of Electronic Imaging

Local minimum squared error for face and handwritten character recognition
Author(s): Zizhu Fan; Jinghua Wang; Qi Zhu; Xiaozhao Fang; Jinrong Cui; Chunhua Li
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The minimum squared error (MSE) for classification is a linear discriminant function-based method that has been used in many applications such as face and handwritten character recognition. Nevertheless, MSE may not deal well with nonlinearly separable data sets. To address this problem, we improve the MSE and propose a new MSE-based algorithm, local MSE (LMSE), which is a local learning algorithm. For a test sample, we first determine its nearest neighbors from the training set. By using the determined neighbors, we construct a local MSE model to predict the class label of the test sample. LMSE can effectively capture the nonlinear structure of the data. It generally outperforms MSE, particularly when the data distribution is nonlinearly separable. Extensive experiments on many nonlinearly separable data sets show that LMSE achieves desirable recognition results.

Paper Details

Date Published: 11 September 2013
PDF: 10 pages
J. Electron. Imag. 22(3) 033027 doi: 10.1117/1.JEI.22.3.033027
Published in: Journal of Electronic Imaging Volume 22, Issue 3
Show Author Affiliations
Zizhu Fan, Harbin Institute of Technology (China)
Jinghua Wang, Harbin Institute of Technology (China)
Qi Zhu, Harbin Institute of Technology (China)
Xiaozhao Fang, Harbin Institute of Technology (China)
Jinrong Cui, Harbin Institute of Technology (China)
Chunhua Li, East China Jiaotong Univ. (China)

© SPIE. Terms of Use
Back to Top