Principal Manifolds and Bayesian Subspaces for Visual Recognition
Abstract
We investigate the use of linear and nonlinear principal manifolds for learning low dimensional representations for visual recognition. Three techniques: principal component analysis (PCA), independent component analysis (ICA) and nonlinear PCA (NLPCA) are examined and tested in a visual recognition experiment using a large gallery of facial images from the "FERET" database. We compare the recognition performance of a nearest neighbour matching rule with each principal manifold representation to that of a maximum a posteriori (MAP) matching rule using a Bayesian similarity measure derived from probabilistic subspaces, and demonstrate the superiority of the latter.
Cite
Text
Moghaddam. "Principal Manifolds and Bayesian Subspaces for Visual Recognition." IEEE/CVF International Conference on Computer Vision, 1999. doi:10.1109/ICCV.1999.790407Markdown
[Moghaddam. "Principal Manifolds and Bayesian Subspaces for Visual Recognition." IEEE/CVF International Conference on Computer Vision, 1999.](https://mlanthology.org/iccv/1999/moghaddam1999iccv-principal/) doi:10.1109/ICCV.1999.790407BibTeX
@inproceedings{moghaddam1999iccv-principal,
title = {{Principal Manifolds and Bayesian Subspaces for Visual Recognition}},
author = {Moghaddam, Baback},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {1999},
pages = {1131-1136},
doi = {10.1109/ICCV.1999.790407},
url = {https://mlanthology.org/iccv/1999/moghaddam1999iccv-principal/}
}