Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant Analysis of Principal Components for Face Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Unified Framework for Subspace Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Embedding: A General Framework for Dimensionality Reduction
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Face Recognition by Stepwise Nonparametric Margin Maximum Criterion
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Journal of Cognitive Neuroscience
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Dual-space linear discriminant analysis for face recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Image Processing
Face recognition using LDA-based algorithms
IEEE Transactions on Neural Networks
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
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Average Neighborhood Margin Maximization (ANMM) is an effective method for feature extraction, especially for addressing the Small Sample Size (SSS) problem. For each specific training sample, ANMM enlarges the margin between itself and its neighbors which are not in its class (heterogeneous neighbors), meanwhile keeps this training sample and its neighbors which belong to the same class (homogeneous neighbor) as close as possible. However, these two requirements are sometimes conflicting in practice. For the purpose of balancing these conflicting requirements and discovering the side information for both the homogeneous neighborhood and the heterogeneous neighborhood, we propose a new type of ANMM in this paper, called Balance ANMM (BANMM). The proposed algorithm not only can enhance the discriminative ability of ANMM, but also can preserve the local structure of training data. Experiments conducted on three well-known face databases i.e. Yale, YaleB and CMU PIE demonstrate the proposed algorithm outperforms ANMM in all three data sets.