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
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A modified algorithm for generalized discriminant analysis
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Discriminant Embedding and Its Variants
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
SIAM Journal on Matrix Analysis and Applications
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
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
Kernel Uncorrelated and Regularized Discriminant Analysis: A Theoretical and Computational Study
IEEE Transactions on Knowledge and Data Engineering
Kernel based nonlinear dimensionality reduction for microarray gene expression data analysis
Expert Systems with Applications: An International Journal
Continuous nonlinear dimensionality reduction by kernel eigenmaps
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
The Kernel Common Vector Method: A Novel Nonlinear Subspace Classifier for Pattern Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
Face recognition using kernel scatter-difference-based discriminant analysis
IEEE Transactions on Neural Networks
Discriminative Common Vector Method With Kernels
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
In this article, the kernel-based methods explained by a graph embedding framework are analyzed and their nature is revealed, i.e. any kernel-based method in a graph embedding framework is equivalent to kernel principal component analysis plus its corresponding linear one. Based on this result, the authors propose a complete kernel-based algorithms framework. Any algorithm in our framework makes full use of two kinds of discriminant information, irregular and regular. The proposed algorithms framework is tested and evaluated using the ORL, Yale and FERET face databases. The experiment results demonstrate the effectiveness of our proposed algorithms framework.