Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Atomic Decomposition by Basis Pursuit
SIAM Review
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Local Discriminant Embedding and Its Variants
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Locality preserving projections
Locality preserving projections
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Correlation Metric for Generalized Feature Extraction
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
Convex and Semi-Nonnegative Matrix Factorizations
IEEE Transactions on Pattern Analysis and Machine Intelligence
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How to define sparse affinity weight matrices is still an open problem in existing manifold learning algorithms. In this paper, we propose a novel unsupervised learning method called Non-negative Sparseness Preserving Embedding (NSPE) for linear dimensionality reduction. Differing from the manifold learning-based subspace learning methods such as Locality Preserving Projections (LPP), Neighbor Preserving Embedding (NPE) and the recently proposed sparse representation based Sparsity Preserving Projections (SPP); NSPE preserves the non-negative sparse reconstruction relationships in low-dimensional subspace. Another novelty of NSPE is the sparseness constraint, which is directly added to control the non-negative sparse representation coefficients. This gives a more ground truth model to imitate the actions of the active neuron cells of V1 of the primate visual cortex on information processing. Although labels are not used in the training steps, the non-negative sparse representation can still discover the latent discriminant information and thus provides better measure coefficients and significant discriminant abilities for feature extraction. Moreover, NSPE is more efficient than the recently proposed sparse representation based SPP algorithm. Comprehensive comparison and extensive experiments show that NSPE has the competitive performance against the unsupervised learning algorithms such as classical PCA and the state-of-the-art techniques: LPP, NPE and SPP.