Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
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
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition using discriminant locality preserving projections
Image and Vision Computing
Short Communication: A novel local preserving projection scheme for use with face recognition
Expert Systems with Applications: An International Journal
Orthogonal Laplacianfaces for Face Recognition
IEEE Transactions on Image Processing
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In this paper, a novel supervised subspace learning algorithm, named local similarity and diversity preserving discriminant projection (LSDDP), is presented. LSDDP defines two weighted adjacency graphs, namely similarity graph and diversity graph. LSDDP constructs the similarity scatter and diversity scatter with the weights, which are adjustable according to the global supervisor and the local semi-supervisor information of the data. Thus LSDDP could utilize both the similarity and diversity information of the data simultaneously for dimensionality reduction. After characterizing the similarity scatter and diversity scatter, a concise feature extraction criterion arised via minimizing the difference between them and the optimal projection is obtained by performing the eigen-decomposition. Thus our method successfully addresses the SSS problem without losing any discriminating information. Finally the proposed model is verified by the face and handwriting digits recognition experiments. The experimental results on Yale, ORL and CMU-PIE face database and the USPS handwriting digits database indicate the effectiveness of our method.