×

L2-loss large-scale linear nonparallel support vector ordinal regression. (Chinese. English summary) Zbl 1438.68118

Summary: Ordinal regression, where the labels of the samples exhibit a natural ordering, is a kind of multi-classification problem. It has wide applications in information retrieval, recommendation systems, and sentiment analysis. With the development of Internet and mobile communication technology, traditional ordinal regression models often underperform when facing numerous large scale, high dimensional and sparse data. However, the nonparallel support vector ordinal regression model shows its advantages with strong adaptability and better performance compared with other SVM-based models. Based on this model, this paper presents a new L2-loss linear nonparallel support vector ordinal regression model, whose linear model could deal with large-scale problems and whose L2-loss could give a great punishment to the sample that deviates from the true label. Besides, two algorithms: trust region Newton method and the dual coordinate descent method (DCD) are developed in terms of different perspectives of the model and their performances are compared. To verify the effectiveness of the model, experiments are conducted on numerous datasets and the results show that the proposed model, especially the model with the DCD algorithm can achieve the state-of-art performance.

MSC:

68T05 Learning and adaptive systems in artificial intelligence
PDFBibTeX XMLCite
Full Text: DOI