Robust classification of graph-based data
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A graph-based classification method is proposed for both semi-supervised learning in the case of Euclidean data and classification in the case of graph data. Our manifold learning technique is based on a convex optimization problem involving a convex quadratic regularization term and a concave quadratic loss function with a trade-off parameter carefully chosen so that the objective function remains convex. As shown empirically, the advantage of considering a concave loss function is that the learning problem becomes more robust in the presence of noisy labels. Furthermore, the loss function considered here is then more similar to a classification loss while several other methods treat graph-based classification problems as regression problems.
KeywordsClassification Graph data Semi-supervised learning
The authors would like to thank the following organizations. EU: The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013) / ERC AdG A-DATADRIVE-B (290923). This paper reflects only the authors’ views, the Union is not liable for any use that may be made of the contained information. Research Council KUL: GOA/10/09 MaNet, CoE PFV/10/002 (OPTEC), BIL12/11T; Ph.D./Postdoc Grants. Flemish Government: FWO: G.0377.12 (Structured systems), G.088114N (Tensor based data similarity); Ph.D./Postdoc Grants. IWT: SBO POM (100031); Ph.D./Postdoc Grants. iMinds Medical Information Technologies SBO 2014. Belgian Federal Science Policy Office: IUAP P7/19 (DYSCO, Dynamical systems, control and optimization, 2012–2017). Fundación BBVA: project FACIL–Ayudas Fundación BBVA a Equipos de Investigación Científica 2016. UAM–ADIC Chair for Data Science and Machine Learning. Concerted Research Action (ARC) programme supported by the Federation Wallonia-Brussels (contract ARC 14/19-060 on Mining and Optimization of Big Data Models).
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