Abstract
Margin Fisher Analysis is a typical graph-based dimensionality reduction technique and has been successfully applied to face recognition. However, it always suffers from the over-fitting, noise, and singular matrix problems. Common preprocessing methods such as PCA lose certain discriminant information in data, which leads the poor classification rate. We propose a novel method called Regularized Margin Fisher Analysis, which decomposes the inter-class similarity matrix into three subspace: principal space, noise space and null space. Then, we regularize the three subspaces in different ways to deal with the noise and over-fitting problems. Moreover, we use twice standard eigendecompositions instead of single generalized eigendecomposition which avoids the singular matrix problem. The experiments on Extended YaleB, CMU PIE and FERET face databases demonstrates that the proposed method is effective and can improve the classification ability.
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Acknowledgments
This work is partially supported by the National Natural Science Foundation of China (61402310). Natural Science Foundation of Jiangsu Province of China (BK20141195).
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Xue, X., Ma, X., Gu, Y., Sun, X., Ni, Z. (2017). A Regularized Margin Fisher Analysis Method for Face Recognition. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10639. Springer, Cham. https://doi.org/10.1007/978-3-319-70136-3_45
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DOI: https://doi.org/10.1007/978-3-319-70136-3_45
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