Neural computation and self-organizing maps: an introduction
Neural computation and self-organizing maps: an introduction
The nature of statistical learning theory
The nature of statistical learning theory
Self-organization as an iterative kernel smoothing process
Neural Computation
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
A Unified Model for Probabilistic Principal Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data visualisation and manifold mapping using the ViSOM
Neural Networks - New developments in self-organizing maps
Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-organizing learning array and its application to economic and financial problems
Information Sciences: an International Journal
Fuzzy integral-based perceptron for two-class pattern classification problems
Information Sciences: an International Journal
Information Sciences: an International Journal
On detecting nonlinear patterns in discriminant problems
Information Sciences: an International Journal
Nonlinear kernel-based statistical pattern analysis
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
Exploiting the performance gains of modern disk drives by enhancing data locality
Information Sciences: an International Journal
5th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services
Tree kernel-based semantic relation extraction with rich syntactic and semantic information
Information Sciences: an International Journal
LPP solution schemes for use with face recognition
Pattern Recognition
PCA based immune networks for human face recognition
Applied Soft Computing
Nonlinear dimensionality reduction using a temporal coherence principle
Information Sciences: an International Journal
Face Recognition Using Kernel UDP
Neural Processing Letters
Geometrically invariant image watermarking using Polar Harmonic Transforms
Information Sciences: an International Journal
Directional discriminant analysis based on nearest feature line
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
Journal of Medical Systems
A twin-hypersphere support vector machine classifier and the fast learning algorithm
Information Sciences: an International Journal
Information Sciences: an International Journal
Fuzzy regularized generalized eigenvalue classifier with a novel membership function
Information Sciences: an International Journal
Kernel self-optimization learning for kernel-based feature extraction and recognition
Information Sciences: an International Journal
Double linear regressions for single labeled image per person face recognition
Pattern Recognition
Local maximal margin discriminant embedding for face recognition
Journal of Visual Communication and Image Representation
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In the recent years, the pattern recognition community paid more attention to a new kind of feature extraction method, the manifold learning methods, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. Among them, locality preserving projection (LPP) is one of the most promising feature extraction techniques. However, when LPP is applied to the classification tasks, it shows some limitations, such as the ignorance of the label information. In this paper, we propose a novel local structure based feature extraction method, called class-wise locality preserving projection (CLPP). CLPP utilizes class information to guide the procedure of feature extraction. In CLPP, the local structure of the original data is constructed according to a certain kind of similarity between data points, which takes special consideration of both the local information and the class information. The kernelized (nonlinear) counterpart of this linear feature extractor is also established in the paper. Moreover, a kernel version of CLPP namely Kernel CLPP (KCLPP) is developed through applying the kernel trick to CLPP to increase its performance on nonlinear feature extraction. Experiments on ORL face database and YALE face database are performed to test and evaluate the proposed algorithm.