L1-Norm Based Linear Discriminant Analysis: An Application to Face Recognition

Wei ZHOU; Sei-ichiro KAMATA

doi:10.1587/transinf.E96.D.550

L1-Norm Based Linear Discriminant Analysis: An Application to Face Recognition

Wei ZHOU, Sei-ichiro KAMATA

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Linear Discriminant Analysis (LDA) is a well-known feature extraction method for supervised subspace learning in statistical pattern recognition. In this paper, a novel method of LDA based on a new L1-norm optimization technique and its variances are proposed. The conventional LDA, which is based on L2-norm, is sensitivity to the presence of outliers, since it used the L2-norm to measure the between-class and within-class distances. In addition, the conventional LDA often suffers from the so-called small sample size (3S) problem since the number of samples is always smaller than the dimension of the feature space in many applications, such as face recognition. Based on L1-norm, the proposed methods have several advantages, first they are robust to outliers because they utilize the L1-norm, which is less sensitive to outliers. Second, they have no 3S problem. Third, they are invariant to rotations as well. The proposed methods are capable of reducing the influence of outliers substantially, resulting in a robust classification. Performance assessment in face application shows that the proposed approaches are more effectiveness to address outliers issue than traditional ones.

Publication: IEICE TRANSACTIONS on Information Vol.E96-D No.3 pp.550-558

Publication Date: 2013/03/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1587/transinf.E96.D.550

Type of Manuscript: Special Section PAPER (Special Section on Face Perception and Recognition)

Category: Face Perception and Recognition

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Wei ZHOU, Sei-ichiro KAMATA, "L1-Norm Based Linear Discriminant Analysis: An Application to Face Recognition" in IEICE TRANSACTIONS on Information, vol. E96-D, no. 3, pp. 550-558, March 2013, doi: 10.1587/transinf.E96.D.550.
Abstract: Linear Discriminant Analysis (LDA) is a well-known feature extraction method for supervised subspace learning in statistical pattern recognition. In this paper, a novel method of LDA based on a new L1-norm optimization technique and its variances are proposed. The conventional LDA, which is based on L2-norm, is sensitivity to the presence of outliers, since it used the L2-norm to measure the between-class and within-class distances. In addition, the conventional LDA often suffers from the so-called small sample size (3S) problem since the number of samples is always smaller than the dimension of the feature space in many applications, such as face recognition. Based on L1-norm, the proposed methods have several advantages, first they are robust to outliers because they utilize the L1-norm, which is less sensitive to outliers. Second, they have no 3S problem. Third, they are invariant to rotations as well. The proposed methods are capable of reducing the influence of outliers substantially, resulting in a robust classification. Performance assessment in face application shows that the proposed approaches are more effectiveness to address outliers issue than traditional ones.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.550/_p

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@ARTICLE{e96-d_3_550,
author={Wei ZHOU, Sei-ichiro KAMATA, },
journal={IEICE TRANSACTIONS on Information},
title={L1-Norm Based Linear Discriminant Analysis: An Application to Face Recognition},
year={2013},
volume={E96-D},
number={3},
pages={550-558},
abstract={Linear Discriminant Analysis (LDA) is a well-known feature extraction method for supervised subspace learning in statistical pattern recognition. In this paper, a novel method of LDA based on a new L1-norm optimization technique and its variances are proposed. The conventional LDA, which is based on L2-norm, is sensitivity to the presence of outliers, since it used the L2-norm to measure the between-class and within-class distances. In addition, the conventional LDA often suffers from the so-called small sample size (3S) problem since the number of samples is always smaller than the dimension of the feature space in many applications, such as face recognition. Based on L1-norm, the proposed methods have several advantages, first they are robust to outliers because they utilize the L1-norm, which is less sensitive to outliers. Second, they have no 3S problem. Third, they are invariant to rotations as well. The proposed methods are capable of reducing the influence of outliers substantially, resulting in a robust classification. Performance assessment in face application shows that the proposed approaches are more effectiveness to address outliers issue than traditional ones.},
keywords={},
doi={10.1587/transinf.E96.D.550},
ISSN={1745-1361},
month={March},}

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TY - JOUR
TI - L1-Norm Based Linear Discriminant Analysis: An Application to Face Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 550
EP - 558
AU - Wei ZHOU
AU - Sei-ichiro KAMATA
PY - 2013
DO - 10.1587/transinf.E96.D.550
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2013
AB - Linear Discriminant Analysis (LDA) is a well-known feature extraction method for supervised subspace learning in statistical pattern recognition. In this paper, a novel method of LDA based on a new L1-norm optimization technique and its variances are proposed. The conventional LDA, which is based on L2-norm, is sensitivity to the presence of outliers, since it used the L2-norm to measure the between-class and within-class distances. In addition, the conventional LDA often suffers from the so-called small sample size (3S) problem since the number of samples is always smaller than the dimension of the feature space in many applications, such as face recognition. Based on L1-norm, the proposed methods have several advantages, first they are robust to outliers because they utilize the L1-norm, which is less sensitive to outliers. Second, they have no 3S problem. Third, they are invariant to rotations as well. The proposed methods are capable of reducing the influence of outliers substantially, resulting in a robust classification. Performance assessment in face application shows that the proposed approaches are more effectiveness to address outliers issue than traditional ones.
ER -