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Subspace learning with frequency regularizer: Its application to face recognition
2015
2015 International Conference on Biometrics (ICB)
Subspace learning is an important technique to enhance the discriminative ability of feature representation and reduce the dimension to improve its efficiency. Due to limited training samples and the usual high-dimensional feature, subspace learning always suffers from overfitting problem, which affects its generalization performance. One possible method is to introduce prior information as a regularizer to constrain its solution space. Traditional regularizers are usually designed in spatial
doi:10.1109/icb.2015.7139113
dblp:conf/icb/LeiYHL15
fatcat:w736uaq4vndr5auv3egb2sbzuu