Local minimum squared error for face and handwritten character recognition

Zizhu Fan; Jinghua Wang; Qi Zhu; Xiaozhao Fang; Jinrong Cui; Chunhua Li

doi:10.1117/1.JEI.22.3.033027

11 September 2013 Local minimum squared error for face and handwritten character recognition

Zizhu Fan, Jinghua Wang, Qi Zhu, Xiaozhao Fang, Jinrong Cui, Chunhua Li

Author Affiliations +

Journal of Electronic Imaging, Vol. 22, Issue 3, 033027 (September 2013). https://doi.org/10.1117/1.JEI.22.3.033027

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.

Citation Download Citation

Zizhu Fan, Jinghua Wang, Qi Zhu, Xiaozhao Fang, Jinrong Cui, and Chunhua Li "Local minimum squared error for face and handwritten character recognition," Journal of Electronic Imaging 22(3), 033027 (11 September 2013). https://doi.org/10.1117/1.JEI.22.3.033027

Published: 11 September 2013

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