Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
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
International Journal of Computer Vision
Learning a Maximum Margin Subspace for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
Local face sketch synthesis learning
Neurocomputing
Discriminative Locality Alignment
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Patch Alignment for Dimensionality Reduction
IEEE Transactions on Knowledge and Data Engineering
Entropy controlled Laplacian regularization for least square regression
Signal Processing
Non-goal scene analysis for soccer video
Neurocomputing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Supervised Gaussian Process Latent Variable Model for Dimensionality Reduction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
Subspaces Indexing Model on Grassmann Manifold for Image Search
IEEE Transactions on Image Processing
m-SNE: Multiview Stochastic Neighbor Embedding
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multiview Hessian discriminative sparse coding for image annotation
Computer Vision and Image Understanding
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It is usually difficult to find the optimal low dimensional subspace for face recognition. Patch alignment framework (PAF) is an important systematic framework that can be applied to understand the common thought and essential differences of a numerous dimensionality reduction algorithms, e.g., principal component analysis, linear discriminant analysis and locally linear embedding and ISOMAP. These algorithms do not consider the intra-class local geometry and the inter-class discrimination simultaneously. In this paper, we present a new dimensionality reduction algorithm based on PAF, termed the discriminative information preservation based dimensionality reduction or DIP for short. First, DIP models the local geometry of intra-class samples by using Locality preserving projection (LPP) rebuilt upon PAF. Second, it models the discriminative information of inter-class samples by maximizing the margin. Thoroughly experimental evidence on several public face datasets suggests the effectiveness of DIP compared with the popular algorithms.