Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
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
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Journal of Cognitive Neuroscience
Wavelet-based 2-parameter regularized discriminant analysis for face recognition
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
An optimization criterion for generalized discriminant analysis on undersampled problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A shape- and texture-based enhanced Fisher classifier for face recognition
IEEE Transactions on Image Processing
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
Face recognition by applying wavelet subband representation and kernel associative memory
IEEE Transactions on Neural Networks
Local Discriminant Wavelet Packet Coordinates for Face Recognition
The Journal of Machine Learning Research
A discriminant analysis for undersampled data
AIDM '07 Proceedings of the 2nd international workshop on Integrating artificial intelligence and data mining - Volume 84
Feature extraction based on Laplacian bidirectional maximum margin criterion
Pattern Recognition
Letters: Feature extraction using fuzzy inverse FDA
Neurocomputing
Efficient computation of PCA with SVD in SQL
Proceedings of the 2nd Workshop on Data Mining using Matrices and Tensors
Incremental learning of bidirectional principal components for face recognition
Pattern Recognition
Two-dimensional maximum margin feature extraction for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Prediction of eigenvalues and regularization of eigenfeatures for human face verification
Pattern Recognition Letters
Feature extraction based on fuzzy 2DLDA
Neurocomputing
A fast method for the implementation of common vector approach
Information Sciences: an International Journal
Feature Extraction Using Laplacian Maximum Margin Criterion
Neural Processing Letters
Kernel based enhanced maximum margin criterion for feature extraction
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
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Many pattern recognition applications involve the treatment of high-dimensional data and the small sample size problem. Principal component analysis (PCA) is a common used dimension reduction technique. Linear discriminate analysis (LDA) is often employed for classification. PCA plus LDA is a famous framework for discriminant analysis in high-dimensional space and singular cases. In this paper, we examine the theory of this framework and find out that even if there is no small sample size problem the PCA dimension reduction cannot guarantee the subsequent successful application of LDA. We thus develop an improved discriminate analysis method by introducing an inverse Fisher criterion and adding a constrain in PCA procedure so that the singularity phenomenon will not occur. Experiment results on face recognition suggest that this new approach works well and can be applied even when the number of training samples is one per class.