Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Supervised nonlinear dimensionality reduction for visualization and classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Feature extraction using constrained maximum variance mapping
Pattern Recognition
A New Orthogonal Discriminant Projection Based Prediction Method for Bioinformatic Data
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Supervised projection approach for boosting classifiers
Pattern Recognition
Two-dimensional supervised local similarity and diversity projection
Pattern Recognition
Orientation distance-based discriminative feature extraction for multi-class classification
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Nearest-neighbor classifier motivated marginal discriminant projections for face recognition
Frontiers of Computer Science in China
Pattern Recognition Letters
Palmprint identification based on wide principal lines
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Journal of Medical Systems
Feature extraction using two-dimensional neighborhood margin and variation embedding
Computer Vision and Image Understanding
Kernel self-optimization learning for kernel-based feature extraction and recognition
Information Sciences: an International Journal
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In the past few years, the computer vision and pattern recognition community has witnessed the rapid growth of a new kind of feature extraction method, the manifold learning methods, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. Among them, locality preserving projection (LPP) is one of the most promising feature extraction techniques. However, when LPP is applied to the classification tasks, it shows some limitations, such as the ignorance of the label information. In this paper, we propose a novel feature extraction method, called locally discriminating projection (LDP). LDP utilizes class information to guide the procedure of feature extraction. In LDP, the local structure of the original data is constructed according to a certain kind of similarity between data points, which takes special consideration of both the local information and the class information. The similarity has several good properties which help to discover the true intrinsic structure of the data, and make LDP a robust technique for the classification tasks. We compare the proposed LDP approach with LPP, as well as other feature extraction methods, such as PCA and LDA, on the public available data sets, FERET and AR. Experimental results suggest that LDP provides a better representation of the class information and achieves much higher recognition accuracies.