IEICE TRANSACTIONS on Information
Exponential Neighborhood Preserving Embedding for Face Recognition
Summary :
Neighborhood preserving embedding is a widely used manifold reduced dimensionality technique. But NPE has to encounter two problems. One problem is that it suffers from the small-sample-size (SSS) problem. Another is that the performance of NPE is seriously sensitive to the neighborhood size k. To overcome the two problems, an exponential neighborhood preserving embedding (ENPE) is proposed in this paper. The main idea of ENPE is that the matrix exponential is introduced to NPE, then the SSS problem is avoided and low sensitivity to the neighborhood size k is gotten. The experiments are conducted on ORL, Georgia Tech and AR face database. The results show that, ENPE shows advantageous performance over other unsupervised methods, such as PCA, LPP, ELPP and NPE. Another is that ENPE is much less sensitive to the neighborhood parameter k contrasted with the unsupervised manifold learning methods LPP, ELPP and NPE.
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.5 pp.1410-1420
- Publication Date
- 2018/05/01
- Publicized
- 2018/01/23
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017EDP7259
- Type of Manuscript
- PAPER
- Category
- Pattern Recognition
Authors
Ruisheng RAN
Chongqing University,Chongqing Normal University
Bin FANG
Chongqing University
Xuegang WU
Yangtse Normal University
Keyword
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Ruisheng RAN, Bin FANG, Xuegang WU, "Exponential Neighborhood Preserving Embedding for Face Recognition" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 5, pp. 1410-1420, May 2018, doi: 10.1587/transinf.2017EDP7259.
Abstract: Neighborhood preserving embedding is a widely used manifold reduced dimensionality technique. But NPE has to encounter two problems. One problem is that it suffers from the small-sample-size (SSS) problem. Another is that the performance of NPE is seriously sensitive to the neighborhood size k. To overcome the two problems, an exponential neighborhood preserving embedding (ENPE) is proposed in this paper. The main idea of ENPE is that the matrix exponential is introduced to NPE, then the SSS problem is avoided and low sensitivity to the neighborhood size k is gotten. The experiments are conducted on ORL, Georgia Tech and AR face database. The results show that, ENPE shows advantageous performance over other unsupervised methods, such as PCA, LPP, ELPP and NPE. Another is that ENPE is much less sensitive to the neighborhood parameter k contrasted with the unsupervised manifold learning methods LPP, ELPP and NPE.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7259/_p
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@ARTICLE{e101-d_5_1410,
author={Ruisheng RAN, Bin FANG, Xuegang WU, },
journal={IEICE TRANSACTIONS on Information},
title={Exponential Neighborhood Preserving Embedding for Face Recognition},
year={2018},
volume={E101-D},
number={5},
pages={1410-1420},
abstract={Neighborhood preserving embedding is a widely used manifold reduced dimensionality technique. But NPE has to encounter two problems. One problem is that it suffers from the small-sample-size (SSS) problem. Another is that the performance of NPE is seriously sensitive to the neighborhood size k. To overcome the two problems, an exponential neighborhood preserving embedding (ENPE) is proposed in this paper. The main idea of ENPE is that the matrix exponential is introduced to NPE, then the SSS problem is avoided and low sensitivity to the neighborhood size k is gotten. The experiments are conducted on ORL, Georgia Tech and AR face database. The results show that, ENPE shows advantageous performance over other unsupervised methods, such as PCA, LPP, ELPP and NPE. Another is that ENPE is much less sensitive to the neighborhood parameter k contrasted with the unsupervised manifold learning methods LPP, ELPP and NPE.},
keywords={},
doi={10.1587/transinf.2017EDP7259},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - Exponential Neighborhood Preserving Embedding for Face Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 1410
EP - 1420
AU - Ruisheng RAN
AU - Bin FANG
AU - Xuegang WU
PY - 2018
DO - 10.1587/transinf.2017EDP7259
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2018
AB - Neighborhood preserving embedding is a widely used manifold reduced dimensionality technique. But NPE has to encounter two problems. One problem is that it suffers from the small-sample-size (SSS) problem. Another is that the performance of NPE is seriously sensitive to the neighborhood size k. To overcome the two problems, an exponential neighborhood preserving embedding (ENPE) is proposed in this paper. The main idea of ENPE is that the matrix exponential is introduced to NPE, then the SSS problem is avoided and low sensitivity to the neighborhood size k is gotten. The experiments are conducted on ORL, Georgia Tech and AR face database. The results show that, ENPE shows advantageous performance over other unsupervised methods, such as PCA, LPP, ELPP and NPE. Another is that ENPE is much less sensitive to the neighborhood parameter k contrasted with the unsupervised manifold learning methods LPP, ELPP and NPE.
ER -
English
