Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
A dimensionality reduction approach to modeling protein flexibility
Proceedings of the sixth annual international conference on Computational biology
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Learning an image manifold for retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
A Two-Stage Linear Discriminant Analysis via QR-Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition with Weighted Locally Linear Embedding
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
Multi-View Face Recognition By Nonlinear Dimensionality Reduction And Generalized Linear Models
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Recognize High Resolution Faces: From Macrocosm to Microcosm
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Visualization of Non-vectorial Data Using Twin Kernel Embedding
AIDM '06 Proceedings of the International Workshop on on Integrating AI and Data Mining
Proceedings of the 24th international conference on Machine learning
Application of Principal Component Analysis to Multikey Searching
IEEE Transactions on Software Engineering
An introduction to nonlinear dimensionality reduction by maximum variance unfolding
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Fast non-negative dimensionality reduction for protein fold recognition
ECML'05 Proceedings of the 16th European conference on Machine Learning
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One of the inherent problems in pattern recognition is the undersampled data problem, also known as the curse of dimensionality reduction. In this paper a new algorithm called pairwise discriminant analysis (PDA) is proposed for pattern recognition. PDA, like linear discriminant analysis (LDA), performs dimensionality reduction and clustering, without suffering from undersampled data to the same extent as LDA.