Orthogonal Neighborhood Preserving Projection using L1-norm Minimization

Purvi A. Koringa, Suman K. Mitra

2017

Abstract

Subspace analysis or dimensionality reduction techniques are becoming very popular for many computer vision tasks including face recognition or in general image recognition. Most of such techniques deal with optimizing a cost function using L2-norm. However, recently, due to capability of handling outliers, optimizing such cost function using L1-norm is drawing the attention of researchers. Present work is the first attempt towards the same goal where Orthogonal Neighbourhood Preserving Projection (ONPP) technique is optimized using L1-norm. In particular the relation of ONPP and PCA is established in the light of L2-norm and then ONPP is optimized using an already proposed mechanism of L1-PCA. Extensive experiments are performed on synthetic as well as real data. It has been observed that L1-ONPP outperforms its counterpart L2-ONPP.

Paper Citation

in Harvard Style

A. Koringa P. and K. Mitra S. (2017). Orthogonal Neighborhood Preserving Projection using L1-norm Minimization . In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 165-172. DOI: 10.5220/0006196101650172

in Bibtex Style

@conference{icpram17,
author={Purvi A. Koringa and Suman K. Mitra},
title={Orthogonal Neighborhood Preserving Projection using L1-norm Minimization},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={165-172},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006196101650172},
isbn={978-989-758-222-6},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Orthogonal Neighborhood Preserving Projection using L1-norm Minimization
SN - 978-989-758-222-6
AU - A. Koringa P.
AU - K. Mitra S.
PY - 2017
SP - 165
EP - 172
DO - 10.5220/0006196101650172