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2018 | OriginalPaper | Chapter

Robust Manifold Learning Based Ordinal Discriminative Correlation Regression

Authors : Qing Tian, Wenqiang Zhang, Liping Wang

Published in: Cloud Computing and Security

Publisher: Springer International Publishing

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Abstract

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.

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Metadata
Title
Robust Manifold Learning Based Ordinal Discriminative Correlation Regression
Authors
Qing Tian
Wenqiang Zhang
Liping Wang
Copyright Year
2018
Book
Cloud Computing and Security

Print ISBN: 978-3-030-00020-2

Electronic ISBN: 978-3-030-00021-9

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-00021-9_60

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