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Authors: Purvi A. Koringa and Suman K. Mitra

Affiliation: Dhirubhai Ambani Institute of Information and Communication Technology, India

Keyword(s): L1-norm, L2-norm, Outliers, Dimensionality Reduction.

Related Ontology Subjects/Areas/Topics: Classification ; Embedding and Manifold Learning ; ICA, PCA, CCA and other Linear Models ; Pattern Recognition ; Theory and Methods

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.

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Paper citation in several formats:
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 - ICPRAM; ISBN 978-989-758-222-6; ISSN 2184-4313, SciTePress, pages 165-172. DOI: 10.5220/0006196101650172

@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 - ICPRAM},
year={2017},
pages={165-172},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006196101650172},
isbn={978-989-758-222-6},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Orthogonal Neighborhood Preserving Projection using L1-norm Minimization
SN - 978-989-758-222-6
IS - 2184-4313
AU - A. Koringa, P.
AU - K. Mitra, S.
PY - 2017
SP - 165
EP - 172
DO - 10.5220/0006196101650172
PB - SciTePress

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