Biologically motivated computationally intensive approaches to image pattern recognition
Future Generation Computer Systems - Special double issue: high performance computing and networking (HPCN)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lip print recognition for security systems by multi-resolution architecture
Future Generation Computer Systems - Special issue: Modeling and simulation in supercomputing and telecommunications
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
Face Recognition Using Laplacianfaces
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
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
A unified framework for semi-supervised dimensionality reduction
Pattern Recognition
Semi-supervised sub-manifold discriminant analysis
Pattern Recognition Letters
Supervised locally linear embedding with probability-based distance for classification
Computers & Mathematics with Applications
Learning a locality discriminating projection for classification
Knowledge-Based Systems
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
Editorial: High performance biometrics recognition algorithms and systems
Future Generation Computer Systems
| Hi-index | 0.00 |
An improved manifold learning method, called enhanced semi-supervised local Fisher discriminant analysis (ESELF), for face recognition is proposed. Motivated by the fact that statistically uncorrelated and parameter-free are two desirable and promising characteristics for dimension reduction, a new difference-based optimization objective function with unlabeled samples has been designed. The proposed method preserves the manifold structure of labeled and unlabeled samples in addition to separating labeled samples in different classes from each other. The semi-supervised method has an analytic form of the globally optimal solution and it can be computed based on eigen decomposition. Experiments on synthetic data and AT&T, Yale and CMU PIE face databases are performed to test and evaluate the proposed algorithm. The experimental results and comparisons demonstrate the effectiveness of the proposed method.