Semi-supervised manifold ordinal regression for image ranking
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Prototype based modelling for ordinal classification
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Neighborhood preserving ordinal regression
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Adaptive metric learning vector quantization for ordinal classification
Neural Computation
Validation based sparse gaussian processes for ordinal regression
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Statistical models and learning algorithms for ordinal regression problems
Information Fusion
A probabilistic least squares approach to ordinal regression
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Exploitation of pairwise class distances for ordinal classification
Neural Computation
Kernelizing the proportional odds model through the empirical kernel mapping
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Can machine learning techniques help to improve the common fisheries policy?
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
An organ allocation system for liver transplantation based on ordinal regression
Applied Soft Computing
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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.