Manifold Modeling with Learned Distance in Random Projection Space for Face Recognition
In this paper, we propose the combination of manifold learning and distance metric learning for the generation of a representation that is both discriminative and informative, and we demonstrate that this approach is effective for face recognition. Initial dimensionality reduction is achieved using random projections, a computationally efficient and data independent linear transformation. Distance metric learning is then applied to increase the separation between classes and improve the accuracy of nearest neighbor classification. Finally, a manifold learning method is used to generate a mapping between the randomly projected data and a low dimensional manifold. Face recognition results suggest that the combination of distance metric learning and manifold learning can increase performance. Furthermore, random projections can be applied as an initial step without significantly affecting the classification accuracy.