Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
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
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Knowledge and Information Systems
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
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
Sparsity preserving projections with applications to face recognition
Pattern Recognition
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Discriminative orthogonal neighborhood-preserving projections for classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Two-dimensional supervised local similarity and diversity projection
Pattern Recognition
Unsupervised learning of image manifolds by semidefinite programming
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Max-Min Distance Analysis by Using Sequential SDP Relaxation for Dimension Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neighborhood preserving regression for image retrieval
Neurocomputing
Manifold elastic net: a unified framework for sparse dimension reduction
Data Mining and Knowledge Discovery
Active Learning Based on Locally Linear Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse transfer learning for interactive video search reranking
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Enhanced fisher discriminant criterion for image recognition
Pattern Recognition
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
Complex Object Correspondence Construction in Two-Dimensional Animation
IEEE Transactions on Image Processing
m-SNE: Multiview Stochastic Neighbor Embedding
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Feature extraction using two-dimensional neighborhood margin and variation embedding
Computer Vision and Image Understanding
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Neighborhood Preserving Embedding (NPE) effectively preserves the geometry of high dimensional data. But, it fails to discover the variation of the values among nearby data, which characterizes the most important modes of variability of patterns. In this paper, we introduce a linear approach, called joint geometry and variability analysis (JGVA), which explicitly considers the geometry and modes of variability of patterns. To be specific, we model the geometrical structure and variability of the local neighborhoods by constructing two adjacency graphs over the training data, and then incorporate the geometry and variability into the objective function of dimensionality reduction. Experiments on four real-world image databases show the effectiveness of the proposed approach.