2016
DOI: 10.1109/tnnls.2015.2434960
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A Maximum Margin Approach for Semisupervised Ordinal Regression Clustering

Abstract: Ordinal regression (OR) is generally defined as the task where the input samples are ranked on an ordinal scale. OR has found a wide variety of applications, and a great deal of work has been done on it. However, most of the existing work focuses on supervised/semisupervised OR classification, and the semisupervised OR clustering problems have not been explicitly addressed. In real-world OR applications, labeling a large number of training samples is usually time-consuming and costly, and instead, a set of unl… Show more

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Cited by 9 publications
(1 citation statement)
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“…This is achievable via other approximation inference methods like variational Bayes and expectation propagation [32,Chapter 10]. From an application view, we can equip ISBOR with other sparse Bayesian architectures and adapt it to other problems like semi-supervised learning [33,8,23] and feature selection [34,35]. From a ranking viewpoint, higher positions are more important.…”
Section: Discussionmentioning
confidence: 99%
“…This is achievable via other approximation inference methods like variational Bayes and expectation propagation [32,Chapter 10]. From an application view, we can equip ISBOR with other sparse Bayesian architectures and adapt it to other problems like semi-supervised learning [33,8,23] and feature selection [34,35]. From a ranking viewpoint, higher positions are more important.…”
Section: Discussionmentioning
confidence: 99%