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
Lambertian Reflectance and Linear Subspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
Graph Embedding: A General Framework for Dimensionality Reduction
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Uncertainty principles and ideal atomic decomposition
IEEE Transactions on Information Theory
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Traditional algorithms for dimensionality reduction attempt to preserve the intrinsic geometric properties from high-dimensional space to low-dimensional space. However, these algorithms have poor discrimination on intersecting data and poorly sampled data for classification tasks, because the distance metric methods used for describing the geometric properties are meaningless when processing under-sampled or intersection data. In this paper, we provide a new perspective on solving the problem of dimensionality reduction and propose a novel and parameterfree algorithm called Sparse Reconstruction Embedding(SRE). In SRE, each point is first reconstructed from all the other points by minimizing the reconstruction errors and L0 norm, and then mapped into lowdimensional coordinates by preserving the minimum of reconstruction errors. Experimental results show that our approach is much more discriminant and insensitive to under-sampled and intersecting data. We also demonstrate that SRE outperforms the state-of-art algorithms both on artificial datasets and natural datasets in classification tasks.