Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Classification of Single Facial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Feature Extraction Based on Decision Boundaries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Optimization in Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A novel SVM+NDA model for classification with an application to face recognition
Pattern Recognition
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This paper introduces a novel Sparse Support Vector Machine model with Kernel Nonparametric Discriminants (SSVMKND) which combines data distribution information from two classifiers, namely, the Kernel Support Vector Machine (KSVM) and the Kernel Nonparametric Discriminant (KND). It is a convex quadratic optimization problem with one global solution, so it can be estimated efficiently with the help of numerical methods. It can also be reduced to the classical KSVM model, and existing SVM programs can be used for easy implementation. We show that our method provides a sparse solution through the Bayesian interpretation. This sparsity can be used by existing sparse classification algorithms to obtain better computational efficiency. The experimental results on real-world datasets and face recognition applications show that the proposed SSVMKND model improves the classification accuracy over other classifiers and also provides sparser solution.