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Kernel Discriminant Learning for Ordinal Regression
June 2010 (vol. 22 no. 6)
pp. 906-910
ASCII Text
x 
 
Bing-Yu Sun, Jiuyong Li, Desheng Dash Wu, Xiao-Ming Zhang, Wen-Bo Li, "Kernel Discriminant Learning for Ordinal Regression," IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 6, pp. 906-910, June, 2010.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2009.170,
author = {Bing-Yu Sun and Jiuyong Li and Desheng Dash Wu and Xiao-Ming Zhang and Wen-Bo Li},
title = {Kernel Discriminant Learning for Ordinal Regression},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {22},
number = {6},
issn = {1041-4347},
year = {2010},
pages = {906-910},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.170},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Kernel Discriminant Learning for Ordinal Regression
IS - 6
SN - 1041-4347
SP906
EP910
EPD - 906-910
A1 - Bing-Yu Sun,
A1 - Jiuyong Li,
A1 - Desheng Dash Wu,
A1 - Xiao-Ming Zhang,
A1 - Wen-Bo Li,
PY - 2010
KW - Ordinal regression
KW - linear discriminant analysis
KW - kernel discriminant analysis.
VL - 22
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Bing-Yu Sun, Chinese Academy of Sciences, Hefei
Jiuyong Li, University of South Australia, Adelaide
Desheng Dash Wu, Reykjavik University, Reykjavik and University of Toronto, Toronto
Xiao-Ming Zhang, Chinese Academy of Sciences, Hefei
Wen-Bo Li, Chinese Academy of Sciences, Hefei
Ordinal regression has wide applications in many domains where the human evaluation plays a major role. Most current ordinal regression methods are based on Support Vector Machines (SVM) and suffer from the problems of ignoring the global information of the data and the high computational complexity. Linear Discriminant Analysis (LDA) and its kernel version, Kernel Discriminant Analysis (KDA), take into consideration the global information of the data together with the distribution of the classes for classification, but they have not been utilized for ordinal regression yet. In this paper, we propose a novel regression method by extending the Kernel Discriminant Learning using a rank constraint. The proposed algorithm is very efficient since the computational complexity is significantly lower than other ordinal regression methods. We demonstrate experimentally that the proposed method is capable of preserving the rank of data classes in a projected data space. In comparison to other benchmark ordinal regression methods, the proposed method is competitive in accuracy.

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Index Terms:
Ordinal regression, linear discriminant analysis, kernel discriminant analysis.
Citation:
Bing-Yu Sun, Jiuyong Li, Desheng Dash Wu, Xiao-Ming Zhang, Wen-Bo Li, "Kernel Discriminant Learning for Ordinal Regression," IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 6, pp. 906-910, June 2010, doi:10.1109/TKDE.2009.170
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