Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-iterative Heteroscedastic Linear Dimension Reduction for Two-Class Data
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Two-Stage Linear Discriminant Analysis via QR-Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis
The Journal of Machine Learning Research
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis
IEEE Transactions on Knowledge and Data Engineering
Stable local dimensionality reduction approaches
Pattern Recognition
Face recognition using discriminant locality preserving projections
Image and Vision Computing
Two-dimensional supervised local similarity and diversity projection
Pattern Recognition
Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multi-manifold discriminant analysis method for image feature extraction
Pattern Recognition
Supervised optimal locality preserving projection
Pattern Recognition
Enhanced fisher discriminant criterion for image recognition
Pattern Recognition
Generalizing discriminant analysis using the generalized singular value decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
Generalized Linear Discriminant Analysis: A Unified Framework and Efficient Model Selection
IEEE Transactions on Neural Networks
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Linear discriminant analysis (LDA) is one of the most popular supervised feature extraction techniques used in machine learning and pattern classification. However, LDA only captures global geometrical structure information of the data and ignores the geometrical structure information of local data points. Though many articles have been published to address this issue, most of them are incomplete in the sense that only part of the local information is used. We show here that there are total three kinds of local information, namely, local similarity information, local intra-class pattern variation, and local inter-class pattern variation. We first propose a new method called enhanced within-class LDA (EWLDA) algorithm to incorporate the local similarity information, and then propose a complete framework called complete global-local LDA (CGLDA) algorithm to incorporate all these three kinds of local information. Experimental results on two image databases demonstrate the effectiveness of our algorithms.