Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning a kernel matrix for nonlinear dimensionality reduction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Direct Method for Building Sparse Kernel Learning Algorithms
The Journal of Machine Learning Research
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-supervised bilinear subspace learning
IEEE Transactions on Image Processing
Discriminative orthogonal neighborhood-preserving projections for classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive kernel principal component analysis
Signal Processing
Unsupervised learning of image manifolds by semidefinite programming
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Tensor distance based multilinear locality-preserved maximum information embedding
IEEE Transactions on Neural Networks
Max-Min Distance Analysis by Using Sequential SDP Relaxation for Dimension Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Kernel Learning for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ensemble Manifold Regularization
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Multimedia
Video Annotation Based on Kernel Linear Neighborhood Propagation
IEEE Transactions on Multimedia
Discriminant Locally Linear Embedding With High-Order Tensor Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Orthogonal Laplacianfaces for Face Recognition
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Topology Preserving Non-negative Matrix Factorization for Face Recognition
IEEE Transactions on Image Processing
Bayesian Tensor Approach for 3-D Face Modeling
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Neural Networks
Non-Negative Patch Alignment Framework
IEEE Transactions on Neural Networks
Manifold Regularized Discriminative Nonnegative Matrix Factorization With Fast Gradient Descent
IEEE Transactions on Image Processing
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Dimensionality reduction algorithms, which aim to obtain low-dimensional feature representation with enhanced discrimination power, have attracted great attention for face recognition. Local Fisher discriminant analysis (LFDA) is a recently developed linear dimensionality reduction algorithm. It has been shown that LFDA is a strong analyzer of high-dimensional data. However, LFDA is a linear method, and this makes it difficult to describe the complex nonlinearity of face images. In addition, LFDA only focuses on using a single data descriptor to depict the whole face image dataset, while lacks a systematic way of integrating multiple image features for dimensionality reduction. To enhance the performance of LFDA in face recognition, a new algorithm termed multiple kernel local Fisher discriminant analysis (MKLFDA) is proposed in this paper. MKLFDA produces nonlinear discriminant features via kernel theory, and considers multiple image features with multiple base kernels. Experimental results on three face databases demonstrate the effectiveness of the proposed algorithm.