Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIAM Journal on Matrix Analysis and Applications
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
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
A Two-Stage Linear Discriminant Analysis via QR-Decomposition
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Orthogonal neighborhood preserving discriminant analysis for face recognition
Pattern Recognition
A Direct Locality Preserving Projections (DLPP) Algorithm for Image Recognition
Neural Processing Letters
A Supervised Subspace Learning Algorithm: Supervised Neighborhood Preserving Embedding
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Complete neighborhood preserving embedding for face recognition
Pattern Recognition
Face recognition using discriminant locality preserving projections
Image and Vision Computing
An optimization criterion for generalized discriminant analysis on undersampled problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Foley-Sammon optimal discriminant vectors using kernel approach
IEEE Transactions on Neural Networks
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
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Recently, many dimensionality reduction algorithms, including local methods and global methods, have been presented. The representative local linear methods are locally linear embedding (LLE) and linear preserving projections (LPP), which seek to find an embedding space that preserves local information to explore the intrinsic characteristics of high dimensional data. However, both of them still fail to nicely deal with the sparsely sampled or noise contaminated datasets, where the local neighborhood structure is critically distorted. On the contrary, principal component analysis (PCA), the most frequently used global method, preserves the total variance by maximizing the trace of feature variance matrix. But PCA cannot preserve local information due to pursuing maximal variance. In order to integrate the locality and globality together and avoid the drawback in LLE and PCA, in this paper, inspired by the dimensionality reduction methods of LLE and PCA, we propose a new dimensionality reduction method for face recognition, namely, unsupervised linear difference projection (ULDP). This approach can be regarded as the integration of a local approach (LLE) and a global approach (PCA), so that it has better performance and robustness in applications. Experimental results on the ORL, YALE and AR face databases show the effectiveness of the proposed method on face recognition.