Description: Following the intuition that the image variation of
faces can be effectively modeled by low dimensional
linear spaces, we propose a novel linear subspace
learning method for face analysis in the framework of
graph embedding model, called Semi-supervised
Graph Embedding (SGE). This algorithm builds an
adjacency graph which can best respect the geometry
structure inferred from the must-link pairwise
constraints, which specify a pair of instances belong to
the same class. The projections are obtained by
preserving such a graph structure. Using the notion of
graph Laplacian, SGE has a closed solution of an
eigen-problem of some specific Laplacian matrix and
therefore it is quite efficient. Experimental results on
Yale standard face database demonstrate the
effectiveness of our proposed algorithm.
- [2DLDA] - Two-dimensional LDA face recognition mat
- [NPE] - Embedding Algorithm maintain neighborhoo
- [SDA] - Semi-supervised classification analysis.
- [Medical_Image_Segmentation] - Medical image segmentation matlab progra
- [colorimageprocess] - This my collection of color image segmen
- [LLE] - This algorithm is a famous and usefule a
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NPE.m
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