Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machines applied to face recognition
Proceedings of the 1998 conference on Advances in neural information processing systems II
Principal Manifolds and Probabilistic Subspaces for Visual Recognition
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Pattern Classification (2nd Edition)
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Graph Embedding: A General Framework for Dimensionality Reduction
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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
Journal of Cognitive Neuroscience
Locality sensitive semi-supervised feature selection
Neurocomputing
Semi-supervised sub-manifold discriminant analysis
Pattern Recognition Letters
Discriminatively regularized least-squares classification
Pattern Recognition
General Solution for Supervised Graph Embedding
ECML '07 Proceedings of the 18th European conference on Machine Learning
Discriminant Analysis Methods for Microarray Data Classification
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Informative Laplacian Projection
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
A doubly weighted approach for appearance-based subspace learning methods
IEEE Transactions on Information Forensics and Security
A novel local sensitive frontier analysis for feature extraction
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Integrating global and local structures in semi-supervised discriminant analysis
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Discriminant analysis via support vectors
Neurocomputing
Regularized locality preserving projections and its extensions for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Face recognition using Intrinsicfaces
Pattern Recognition
Pattern Recognition Letters
Orthogonal discriminant local tangent space alignment
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Localized twin SVM via convex minimization
Neurocomputing
Semi-supervised locally discriminant projection for classification and recognition
Knowledge-Based Systems
Dimensionality reduction by minimizing nearest-neighbor classification error
Pattern Recognition Letters
Kernel Discriminant Embedding in face recognition
Journal of Visual Communication and Image Representation
Supervised optimal locality preserving projection
Pattern Recognition
Investigation of supervised dimensionality reduction methods for phonetic classification
Proceedings of the Third International Conference on Internet Multimedia Computing and Service
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Semi-supervised classification based on random subspace dimensionality reduction
Pattern Recognition
Expert Systems with Applications: An International Journal
Nearest-neighbor classifier motivated marginal discriminant projections for face recognition
Frontiers of Computer Science in China
Gender from body: a biologically-inspired approach with manifold learning
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Human action recognition using spatio-temporal classification
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Feature relationships hypergraph for multimodal recognition
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Human action recognition based on graph-embedded spatio-temporal subspace
Pattern Recognition
Enhanced fisher discriminant criterion for image recognition
Pattern Recognition
Statistical Analysis and Data Mining
Two-Dimensional locality discriminant projection for plant leaf classification
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Robust sparse bounding sphere for 3D face recognition
Image and Vision Computing
Pose-invariant face recognition in videos for human-machine interaction
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Generalized locality preserving Maxi-Min Margin Machine
Neural Networks
Supervised patient similarity measure of heterogeneous patient records
ACM SIGKDD Explorations Newsletter
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Feature extraction using two-dimensional neighborhood margin and variation embedding
Computer Vision and Image Understanding
Muscle computer interfaces for driver distraction reduction
Computer Methods and Programs in Biomedicine
Parameterless Local Discriminant Embedding
Neural Processing Letters
Local maximal margin discriminant embedding for face recognition
Journal of Visual Communication and Image Representation
A Family of Discriminative Manifold Learning Algorithms and Their Application to Speech Recognition
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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Linear Discriminant Analysis (LDA) is a popular data-analytic tool for studying the class relationship between data points. A major disadvantage of LDA is that it fails to discover the local geometrical structure of the data manifold. In this paper, we introduce a novel linear algorithm for discriminant analysis, called Locality Sensitive Discriminant Analysis (LSDA). When there is no sufficient training samples, local structure is generally more important than global structure for discriminant analysis. By discovering the local manifold structure, LSDA finds a projection which maximizes the margin between data points from different classes at each local area. Specifically, the data points are mapped into a subspace in which the nearby points with the same label are close to each other while the nearby points with different labels are far apart. Experiments carried out on several standard face databases show a clear improvement over the results of LDA-based recognition.