The nature of statistical learning theory
The nature of statistical learning theory
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
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine Learning
Extended isomap for pattern classification
Eighteenth national conference on Artificial intelligence
SIAM Journal on Matrix Analysis and Applications
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Face Recognition Using Kernel Based Fisher Discriminant Analysis
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Regularized discriminant analysis for the small sample size problem in face recognition
Pattern Recognition Letters
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIAM Journal on Matrix Analysis and Applications
2D and 3D face recognition: A survey
Pattern Recognition Letters
Journal of Cognitive Neuroscience
Isotropic PCA and Affine-Invariant Clustering
FOCS '08 Proceedings of the 2008 49th Annual IEEE Symposium on Foundations of Computer Science
Orthogonalized discriminant analysis based on generalized singular value decomposition
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Novel IPCA-Based Classifiers and Their Application to Spam Filtering
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Heteroscedastic Multilinear Discriminant Analysis for Face Recognition
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
An optimization criterion for generalized discriminant analysis on undersampled problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving kernel Fisher discriminant analysis for face recognition
IEEE Transactions on Circuits and Systems for Video Technology
Modelling political disaffection from Twitter data
Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining
Integrated Fisher linear discriminants: An empirical study
Pattern Recognition
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At the present, several applications need to classify high dimensional points belonging to highly unbalanced classes. Unfortunately, when the training set cardinality is small compared to the data dimensionality (''small sample size'' problem) the classification performance of several well-known classifiers strongly decreases. Similarly, the classification accuracy of several discriminative methods decreases when non-linearly separable, and unbalanced, classes are treated. In this paper we firstly survey state of the art methods that employ improved versions of Linear Discriminant Analysis (LDA) to deal with the above mentioned problems; secondly, we propose a family of classifiers based on the Fisher subspace estimation, which efficiently deal with the small sample size problem, non-linearly separable classes, and unbalanced classes. The promising results obtained by the proposed techniques on benchmark datasets and the comparison with state of the art predictors show the efficacy of the proposed techniques.