Robust classification of graph-based data

• Autores: Carlos María Alaiz Gudín, Michaël Fanuel, Johan A. K. Suykens
• Localización: Data mining and knowledge discovery, ISSN 1384-5810, Vol. 33, Nº 1, 2019, págs. 230-251
• Idioma: inglés
• DOI: 10.1007/s10618-018-0603-9
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• Resumen

  • 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.