Fast Outlier Detection in High Dimensional Spaces
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Non-linear dimensionality reduction techniques for classification and visualization
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The CMU Pose, Illumination, and Expression (PIE) Database
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
A facial expression recognition system based on supervised locally linear embedding
Pattern Recognition Letters
Robust locally linear embedding
Pattern Recognition
Neighborhood discriminant projection for face recognition
Pattern Recognition Letters
Probability-Based Locally Linear Embedding for Classification
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
Locally linear discriminant embedding: An efficient method for face recognition
Pattern Recognition
Weighted locally linear embedding for dimension reduction
Pattern Recognition
Supervised locally linear embedding
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Video-based face recognition using probabilistic appearance manifolds
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Parameterless isomap with adaptive neighborhood selection
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Supervised nonlinear dimensionality reduction for visualization and classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Regularization parameter choice in locally linear embedding
Neurocomputing
Guided Locally Linear Embedding
Pattern Recognition Letters
Locally linear embedding: a survey
Artificial Intelligence Review
Towards collaborative feature extraction for face recognition
Natural Computing: an international journal
A comparative study of nonlinear manifold learning methods for cancer microarray data classification
Expert Systems with Applications: An International Journal
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In this paper, a new nonlinear dimensionality reduction algorithm, called enhanced supervised locally linear embedding (ESLLE), is proposed. The ESLLE method attempts to make the interclass dissimilarity definitely larger than the intraclass dissimilarity in an effort to strengthen the discriminating power and generalization ability of embedded data representation. Simulation studies on artificial manifold data demonstrate that ESLLE can give better embedding results in dimensionality reduction and is more robust to noise in comparison with the original supervised LLE (SLLE). Experimental results on extended Yale face database B and CMU PIE face databases demonstrate that ESLLE obtains better performance on face recognition compared with other famous methods such as principal component analysis (PCA), locally linear embedding (LLE) as well as SLLE.