Ordinal regression (OR) is a special multiclass classification problem where
an order relation exists among the labels. Recent years, people share their
opinions and sentimental judgments conveniently with social networks and
E-Commerce so that plentiful large-scale OR problems arise. However, few
studies have focused on this kind of problems. Nonparallel Support Vector
Ordinal Regression (NPSVOR) is a SVM-based OR model, which learns a hyperplane
for each rank by solving a series of independent sub-optimization problems and
then ensembles those learned hyperplanes to predict. The previous studies are
focused on its nonlinear case and got a competitive testing performance, but
its training is time consuming, particularly for large-scale data. In this
paper, we consider NPSVOR’s linear case and design an efficient training method
based on the dual coordinate descent method (DCD). To utilize the order
information among labels in prediction, a new prediction function is also
proposed. Extensive contrast experiments on the text OR datasets indicate that
the carefully implemented DCD is very suitable for training large data.

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