Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
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
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
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
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Pedestrian Detection via Classification on Riemannian Manifolds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparsity preserving projections with applications to face recognition
Pattern Recognition
Sparsity preserving discriminant analysis for single training image face recognition
Pattern Recognition Letters
Learning with l1-graph for image analysis
IEEE Transactions on Image Processing
Inferring 3D body pose from silhouettes using activity manifold learning
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
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Manifold learning is a novel approach in non-linear dimensionality reduction that has shown great potential in numerous applications and has gained ground compared to linear techniques. In addition, sparse representations have been recently applied on computer vision problems with success, demonstrating promising results with respect to robustness in challenging scenarios. A key concept shared by both approaches is the notion of sparsity. In this paper we investigate how the framework of sparse representations can be applied in various stages of manifold learning. We explore the use of sparse representations in two major components of manifold learning: construction of the weight matrix and classification of test data. In addition, we investigate the benefits that are offered by introducing a weighting scheme on the sparse representations framework via the weighted LASSO algorithm. The underlying manifold learning approach is based on the recently proposed spectral regression framework that offers significant benefits compared to previously proposed manifold learning techniques. We present experimental results on these techniques in three challenging face recognition datasets.