The FERET Evaluation Methodology for Face-Recognition Algorithms
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
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
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
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Flexible manifold embedding: a framework for semi-supervised and unsupervised dimension reduction
IEEE Transactions on Image Processing
Motion segmentation with missing data using power factorization and GPCA
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Dimensionality reduction by Mixed Kernel Canonical Correlation Analysis
Pattern Recognition
Locally Discriminative Coclustering
IEEE Transactions on Knowledge and Data Engineering
Discriminant Locally Linear Embedding With High-Order Tensor Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Effective Feature Extraction in High-Dimensional Space
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Supervised Gaussian Process Latent Variable Model for Dimensionality Reduction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Semisupervised Dimensionality Reduction and Classification Through Virtual Label Regression
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Self-taught dimensionality reduction on the high-dimensional small-sized data
Pattern Recognition
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In recent years, dimensionality reduction has attracted a great deal of attention in the communities of machine learning and data mining. The basic goal of dimensionality reduction is to discover the low dimensional manifold embedded in a high dimensional space. Although some existing manifold learning algorithms (ISOMAP, LE, LLE, LTSA, etc.) can capture the local structure of data manifold, they have poor performance in some recognition tasks. This is mainly because that they cannot handle well with the ''out of sample'' problem. Moreover, these algorithms are sensitive to the choice of nearest neighbors, which is crucial in classification. To address these problems, this paper proposes a Robust Dimensionality Reduction Algorithm With Local and Global Structure (RLGS) based on a novel adaptive weighting mechanism. Hybrid structure of local and global structures is studied. By using the adaptive weight, RLGS has the capacity of adaptively exploiting non-linear structure of data manifold and is robust to parameters. Experiments demonstrate that RLGS performs better on public face databases compared with other reported algorithms.