Robust Manifold Learning Based Ordinal Discriminative Correlation Regression

Canonical correlation analysis (CCA) is a typical learning paradigm of capturing the correlation components across multi-views of the same data. When countered with such data with ordinal labels, the accuracy performance yielded by traditional CCA is usually not desirable because of ignoring the ordinal relationships among data labels. In order to incorporate the ordinal information into the objective function of CCA, the so-called ordinal discriminative CCA (OR-DisCCA) was presented. Although OR-DisCCA can yield better ordinal regression results, its performance will be deteriorated when the data are corrupted with outliers because the ordered class centers easily tend to be biased by the outliers. To address this issue, in this work we construct robust manifold ordinal discriminative correlation regression (rmODCR) by replacing the traditional (\(l_2\)-norm) class centers with \(l_p\)-norm centers in objective optimization. Finally, we experimentally evaluate the effectiveness of the proposed method.