Issue No. 06 - June (2010 vol. 22)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.170
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.
Ordinal regression, linear discriminant analysis, kernel discriminant analysis.
W. Li, D. D. Wu, X. Zhang, B. Sun and J. Li, "Kernel Discriminant Learning for Ordinal Regression," in IEEE Transactions on Knowledge & Data Engineering, vol. 22, no. , pp. 906-910, 2009.