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Local minimum squared error for face and handwritten character recognition

[+] Author Affiliations
Zizhu Fan

Harbin Institute of Technology, Shenzhen Graduate School, Bio-Computing Research Center, Shenzhen 518055, China

Key Laboratory of Network Oriented Intelligent Computation, Shenzhen 518055, China

East China Jiaotong University, School of Basic Science, Nanchang 330013, China

Jinghua Wang

Harbin Institute of Technology, Shenzhen Graduate School, Bio-Computing Research Center, Shenzhen 518055, China

Key Laboratory of Network Oriented Intelligent Computation, Shenzhen 518055, China

Qi Zhu

Harbin Institute of Technology, Shenzhen Graduate School, Bio-Computing Research Center, Shenzhen 518055, China

Key Laboratory of Network Oriented Intelligent Computation, Shenzhen 518055, China

Xiaozhao Fang

Harbin Institute of Technology, Shenzhen Graduate School, Bio-Computing Research Center, Shenzhen 518055, China

Key Laboratory of Network Oriented Intelligent Computation, Shenzhen 518055, China

Jinrong Cui

Harbin Institute of Technology, Shenzhen Graduate School, Bio-Computing Research Center, Shenzhen 518055, China

Key Laboratory of Network Oriented Intelligent Computation, Shenzhen 518055, China

Chunhua Li

East China Jiaotong University, School of Basic Science, Nanchang 330013, China

J. Electron. Imaging. 22(3), 033027 (Sep 11, 2013). doi:10.1117/1.JEI.22.3.033027
History: Received March 3, 2013; Revised July 20, 2013; Accepted August 21, 2013
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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.

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© 2013 SPIE and IS&T

Citation

Zizhu Fan ; Jinghua Wang ; Qi Zhu ; Xiaozhao Fang ; Jinrong Cui, et al.
"Local minimum squared error for face and handwritten character recognition", J. Electron. Imaging. 22(3), 033027 (Sep 11, 2013). ; http://dx.doi.org/10.1117/1.JEI.22.3.033027


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