Ordinal Support Vector Regression (OSVR) is a general purpose regression model that takes data samples as well as their pairwise ordinal relation as input and output the model parameters learned from data under the max-margin framework. Label (regression response) of each individual sample is not required. OSVR can adapt from no supervision (no labels) to full supervision (labels on all samples). In particular, OSVR can be very useful under weak supervision, where only labels on selected key samples are provided. However, ordinal information should always be available under different supervision settings. Current version 1.0 only supports linear regression model.
This repository provides an implementation of OSVR in Matlab. The core optimization problem is solved using customized Alternating Direction Method of Multipliers (ADMM). However, the 'admm' function is implemented general enough to handle an optimization problem with standard form. See related publication for more details.
To see an example, run 'main' script. The sample data provided correspond to one testing fold of UNBC-McMaster shoulder pain dataset [1]. Please contact author if you need more data for comparison purpose.
If you use the code in your research, please consider citing following publication
Rui Zhao, Quan Gan, Shangfei Wang and Qiang Ji. "Facial Expression Intensity Estimation Using Ordinal Information." IEEE Conference on Computer Vision and Pattern Recognition. 2016. [PDF] [Bib] [Supplement]
[1] P. Lucey, J. F. Cohn, K. M. Prkachin, P. E. Solomon, and I. Matthews. Painful data: The unbc-mcmaster shoulder pain expression archive database. In FG, pages 57–64. IEEE, 2011.
Copyright (C) 2016 Rui Zhao
Distibution code version 1.0 - 06/25/2016. This code is for research purpose only.