Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Learning a Locality Preserving Subspace for Visual Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization
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
Robust locally linear embedding
Pattern Recognition
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning a Maximum Margin Subspace for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
Principal Component Analysis Based on L1-Norm Maximization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Weighted locally linear embedding for dimension reduction
Pattern Recognition
Iterative subspace analysis based on feature line distance
IEEE Transactions on Image Processing
Neighborhood preserving projections (NPP): a novel linear dimension reduction method
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Constructing PCA Baseline Algorithms to Reevaluate ICA-Based Face-Recognition Performance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Discriminant Locally Linear Embedding With High-Order Tensor Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Locality Versus Globality: Query-Driven Localized Linear Models for Facial Image Computing
IEEE Transactions on Circuits and Systems for Video Technology
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
Feature extraction based on Lp-norm generalized principal component analysis
Pattern Recognition Letters
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Graph embedding is a general framework for subspace learning. However, because of the well-known outlier-sensitiveness disadvantage of the L2-norm, conventional graph embedding is not robust to outliers which occur in many practical applications. In this paper, an improved graph embedding algorithm (termed LPP-L1) is proposed by replacing L2-norm with L1-norm. In addition to its robustness property, LPP-L1 avoids small sample size problem. Experimental results on both synthetic and real-world data demonstrate these advantages.