IEICE TRANSACTIONS on Information
Learning a Two-Dimensional Fuzzy Discriminant Locality Preserving Subspace for Visual Recognition
Summary :
A novel Two-dimensional Fuzzy Discriminant Locality Preserving Projections (2D-FDLPP) algorithm is proposed for learning effective subspace of two-dimensional images. The 2D-FDLPP algorithm is derived from the Two-dimensional Locality Preserving Projections (2D-LPP) by exploiting both fuzzy and discriminant properties. 2D-FDLPP algorithm preserves the relationship degree of each sample belonging to given classes with fuzzy k-nearest neighbor classifier. Also, it introduces between-class scatter constrain and label information into 2D-LPP algorithm. 2D-FDLPP algorithm finds the subspace which can best discriminate different pattern classes and weakens the environment factors according to soft assignment method. Therefore, 2D-FDLPP algorithm has more discriminant power than 2D-LPP, and is more suitable for recognition tasks. Experiments are conducted on the MNIST database for handwritten image classification, the JAFFE database and Cohn-Kanade database for facial expression recognition and the ORL database for face recognition. Experimental results reported the effectiveness of our proposed algorithm.
- Publication
- IEICE TRANSACTIONS on Information Vol.E97-D No.9 pp.2434-2442
- Publication Date
- 2014/09/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2013EDP7422
- Type of Manuscript
- PAPER
- Category
- Pattern Recognition
Authors
Ruicong ZHI
Beijing Jiaotong University,China National Institute of Standardization
Lei ZHAO
China National Institute of Standardization
Bolin SHI
China National Institute of Standardization
Yi JIN
Beijing Jiaotong University
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Ruicong ZHI, Lei ZHAO, Bolin SHI, Yi JIN, "Learning a Two-Dimensional Fuzzy Discriminant Locality Preserving Subspace for Visual Recognition" in IEICE TRANSACTIONS on Information,
vol. E97-D, no. 9, pp. 2434-2442, September 2014, doi: 10.1587/transinf.2013EDP7422.
Abstract: A novel Two-dimensional Fuzzy Discriminant Locality Preserving Projections (2D-FDLPP) algorithm is proposed for learning effective subspace of two-dimensional images. The 2D-FDLPP algorithm is derived from the Two-dimensional Locality Preserving Projections (2D-LPP) by exploiting both fuzzy and discriminant properties. 2D-FDLPP algorithm preserves the relationship degree of each sample belonging to given classes with fuzzy k-nearest neighbor classifier. Also, it introduces between-class scatter constrain and label information into 2D-LPP algorithm. 2D-FDLPP algorithm finds the subspace which can best discriminate different pattern classes and weakens the environment factors according to soft assignment method. Therefore, 2D-FDLPP algorithm has more discriminant power than 2D-LPP, and is more suitable for recognition tasks. Experiments are conducted on the MNIST database for handwritten image classification, the JAFFE database and Cohn-Kanade database for facial expression recognition and the ORL database for face recognition. Experimental results reported the effectiveness of our proposed algorithm.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2013EDP7422/_p
Copy
@ARTICLE{e97-d_9_2434,
author={Ruicong ZHI, Lei ZHAO, Bolin SHI, Yi JIN, },
journal={IEICE TRANSACTIONS on Information},
title={Learning a Two-Dimensional Fuzzy Discriminant Locality Preserving Subspace for Visual Recognition},
year={2014},
volume={E97-D},
number={9},
pages={2434-2442},
abstract={A novel Two-dimensional Fuzzy Discriminant Locality Preserving Projections (2D-FDLPP) algorithm is proposed for learning effective subspace of two-dimensional images. The 2D-FDLPP algorithm is derived from the Two-dimensional Locality Preserving Projections (2D-LPP) by exploiting both fuzzy and discriminant properties. 2D-FDLPP algorithm preserves the relationship degree of each sample belonging to given classes with fuzzy k-nearest neighbor classifier. Also, it introduces between-class scatter constrain and label information into 2D-LPP algorithm. 2D-FDLPP algorithm finds the subspace which can best discriminate different pattern classes and weakens the environment factors according to soft assignment method. Therefore, 2D-FDLPP algorithm has more discriminant power than 2D-LPP, and is more suitable for recognition tasks. Experiments are conducted on the MNIST database for handwritten image classification, the JAFFE database and Cohn-Kanade database for facial expression recognition and the ORL database for face recognition. Experimental results reported the effectiveness of our proposed algorithm.},
keywords={},
doi={10.1587/transinf.2013EDP7422},
ISSN={1745-1361},
month={September},}
Copy
TY - JOUR
TI - Learning a Two-Dimensional Fuzzy Discriminant Locality Preserving Subspace for Visual Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 2434
EP - 2442
AU - Ruicong ZHI
AU - Lei ZHAO
AU - Bolin SHI
AU - Yi JIN
PY - 2014
DO - 10.1587/transinf.2013EDP7422
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2014
AB - A novel Two-dimensional Fuzzy Discriminant Locality Preserving Projections (2D-FDLPP) algorithm is proposed for learning effective subspace of two-dimensional images. The 2D-FDLPP algorithm is derived from the Two-dimensional Locality Preserving Projections (2D-LPP) by exploiting both fuzzy and discriminant properties. 2D-FDLPP algorithm preserves the relationship degree of each sample belonging to given classes with fuzzy k-nearest neighbor classifier. Also, it introduces between-class scatter constrain and label information into 2D-LPP algorithm. 2D-FDLPP algorithm finds the subspace which can best discriminate different pattern classes and weakens the environment factors according to soft assignment method. Therefore, 2D-FDLPP algorithm has more discriminant power than 2D-LPP, and is more suitable for recognition tasks. Experiments are conducted on the MNIST database for handwritten image classification, the JAFFE database and Cohn-Kanade database for facial expression recognition and the ORL database for face recognition. Experimental results reported the effectiveness of our proposed algorithm.
ER -
English
