A regularization path algorithm for support vector ordinal regression

作者：Gu, Bin^*

来源：Neural Networks, 2018, 98: 114-121.

DOI：10.1016/j.neunet.2017.11.008

• Solution path
• Support vector ordinal regression
• QR decomposition
• Singularity

Support vector ordinal regression (SVOR) is a popular method for tackling ordinal regression problems. Solution path provides a compact representation of optimal solutions for all values of regularization parameter, which is extremely useful for model selection. However, due to the complicated formulation of SVOR (including multiple equalities and extra variables), there is still no solution path algorithm proposed for SVOR. In this paper, we propose a regularization path algorithm for SVOR which can track the two sets of variables of SVOR w.r.t. the regularization parameter. Technically, we use the QR decomposition to handle the singular matrices in the regularization path. Experiment results on a variety of datasets not only confirm the effectiveness of our regularization path algorithm, but also show the superiority of our regularization path algorithm on model selection.

• 出版日期2018-2
• 单位南京信息工程大学