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
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
CEDD: color and edge directivity descriptor: a compact descriptor for image indexing and retrieval
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
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As a widely used linear dimensionality reduction technique, Locality Preserving Projections (LPP) preserves the neighborhood structure of the dataset by finding the optimal linear approximations to the eigenfunctions of the Laplace-Beltrami operator on the manifold, which makes it have several advantages of both linear and nonlinear methods. However, its neighborhood graph is generated by adopting the Euclidean distance as the similarity metric of different samples which leads to the unsatisfying effectiveness of LPP. To address the limitation of Euclidean distance we propose an improved LPP called Manifold Ranking-based LPP (MRLPP) which can effectively preserve the neighborhood structure of the dataset, either globular or non-globular. Experimental results on several datasets demonstrate the effectiveness of our method.