Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mapping a manifold of perceptual observations
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
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
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
Nonlocal Estimation of Manifold Structure
Neural Computation
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Label Propagation through Linear Neighborhoods
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph construction and b-matching for semi-supervised learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Sparsity preserving projections with applications to face recognition
Pattern Recognition
On Defining Partition Entropy by Inequalities
IEEE Transactions on Information Theory
Orthogonal Laplacianfaces for Face Recognition
IEEE Transactions on Image Processing
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Optimal Locality Regularized Least Squares Support Vector Machine via Alternating Optimization
Neural Processing Letters
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Graph optimization for dimensionality reduction with sparsity constraints
Pattern Recognition
Nearest-neighbor classifier motivated marginal discriminant projections for face recognition
Frontiers of Computer Science in China
A supervised non-linear dimensionality reduction approach for manifold learning
Pattern Recognition
Semi-supervised fuzzy clustering with metric learning and entropy regularization
Knowledge-Based Systems
Journal of Medical Systems
Automatic dimensionality estimation for manifold learning through optimal feature selection
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Embedding new observations via sparse-coding for non-linear manifold learning
Pattern Recognition
Regularized discriminant entropy analysis
Pattern Recognition
Dimensionality reduction with adaptive graph
Frontiers of Computer Science: Selected Publications from Chinese Universities
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Locality preserving projections (LPP) is a typical graph-based dimensionality reduction (DR) method, and has been successfully applied in many practical problems such as face recognition. However, LPP depends mainly on its underlying neighborhood graph whose construction suffers from the following issues: (1) such neighborhood graph is artificially defined in advance, and thus does not necessary benefit subsequent DR task; (2) such graph is constructed using the nearest neighbor criterion which tends to work poorly due to the high-dimensionality of original space; (3) it is generally uneasy to assign appropriate values for the neighborhood size and heat kernel parameter involved in graph construction. To address these problems, we develop a novel DR algorithm called Graph-optimized Locality Preserving Projections (GoLPP). The idea is to integrate graph construction with specific DR process into a unified framework, which results in an optimized graph rather than predefined one. Moreover, an entropy regularization term is incorporated into the objective function for controlling the uniformity level of the edge weights in graph, so that a principled graph updating formula naturally corresponding to conventional heat kernel weights can be obtained. Finally, the experiments on several publicly available UCI and face data sets show the feasibility and effectiveness of the proposed method with encouraging results.