Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Empirical Performance Analysis of Linear Discriminant Classifiers
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Using Uncorrelated Discriminant Analysis for Tissue Classification with Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Discriminant Embedding and Its Variants
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Local Fisher discriminant analysis for supervised dimensionality reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Eigenfeature Regularization and Extraction in Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Extraction and Uncorrelated Discriminant Analysis for High-Dimensional Data
IEEE Transactions on Knowledge and Data Engineering
Nonparametric Discriminant Analysis for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph-optimized locality preserving projections
Pattern Recognition
Supervised optimal locality preserving projection
Pattern Recognition
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
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High-dimensionality of data and the small sample size problem are two significant limitations for applying subspace methods which are favored by face recognition. In this paper, a new linear dimension reduction method called locally uncorrelated discriminant projections (LUDP) is proposed, which addresses the two problems from a new aspect. More specifically, we propose a locally uncorrelated criterion, which aims to decorrelate learned discriminant factors over data locally rather than globally. It has been shown that the statistical uncorrelation criterion is an important property for reducing dimension and learning robust discriminant projection as well. However, data are always locally distributed, so it is more important to explore locally statistical uncorrelated discriminant information over data. We impose this new constraint into a graph-based maximum margin analysis, so that LUDP also characterizes the local scatter as well as nonlocal scatter, seeking to find a projection that maximizes the difference, rather than the ratio between the nonlocal scatter and the local scatter. Experiments on ORL, Yale, Extended Yale face database B and FERET face database demonstrate the effectiveness of our proposed method.