Robust Learning of Discriminative Projection for Multicategory Classification on the Stiefel Manifold
Abstract
Learning a robust projection with a small number of training samples is still a challenging problem in face recognition, especially when the unseen faces have extreme variation in pose, illumination, and facial expression. To address this problem, we propose a framework formulated under statistical learning theory that facilitates robust learning of a discriminative projection. Dimensionality reduction using the projection matrix is combined with a linear classifier in the regularized framework of lasso regression. The projection matrix in conjunction with the classifier parameters are then found by solving an optimization problem over the Stiefel manifold. The experimental results on standard face databases suggest that the proposed method outperforms some recent regularized techniques when the number of training samples is small.
Cite
Text
Pham and Venkatesh. "Robust Learning of Discriminative Projection for Multicategory Classification on the Stiefel Manifold." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008. doi:10.1109/CVPR.2008.4587407Markdown
[Pham and Venkatesh. "Robust Learning of Discriminative Projection for Multicategory Classification on the Stiefel Manifold." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008.](https://mlanthology.org/cvpr/2008/pham2008cvpr-robust/) doi:10.1109/CVPR.2008.4587407BibTeX
@inproceedings{pham2008cvpr-robust,
title = {{Robust Learning of Discriminative Projection for Multicategory Classification on the Stiefel Manifold}},
author = {Pham, Duc-Son and Venkatesh, Svetha},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2008},
doi = {10.1109/CVPR.2008.4587407},
url = {https://mlanthology.org/cvpr/2008/pham2008cvpr-robust/}
}