An Inductive Framework for Semi-supervised Learning (Discussion Paper)
Contributo in Atti di convegno
Data di Pubblicazione:
2021
Abstract:
Semi-supervised learning is crucial in many applications where accessing class labels is unaffordable or costly. The most promising approaches are graph-based but they are transductive and they do not provide a generalized model working on inductive scenarios. To address this problem, we propose a generic framework for inductive semi-supervised learning based on three components: an ensemble of semi-supervised autoencoders providing a new data representation that leverages the knowledge supplied by the reduced amount of available labels; a graph-based step that helps augmenting the training set with pseudo-labeled instances and, finally, a classifier trained with labeled and pseudo-labeled instances. The experimental results show that our framework outperforms state-of-the-art inductive semi-supervised methods.
Tipologia CRIS:
04A-Conference paper in volume
Keywords:
semi-supervised learning, graph-based algorithms, inductive methods
Elenco autori:
Shuyi Yang, Dino Ienco, Roberto Esposito, Ruggero G. Pensa
Link alla scheda completa:
Titolo del libro:
Proceedings of the 29th Italian Symposium on Advanced Database Systems (SEBD 2021)
Pubblicato in: