A note on label propagation for semi-supervised learning.

*(English)*Zbl 1334.68178Summary: Semi-supervised learning has become an important and thoroughly studied subdomain of machine learning in the past few years, because gathering large unlabeled data is almost costless, and the costly human labeling process can be minimized by semi-supervision. Label propagation is a transductive semi-supervised learning method that operates on the – most of the time undirected – data graph. It was introduced in [X. Zhu and Z. Ghahramani, Learning from labeled and unlabeled data with label propagation. Techn. Rep., Carnegie Mellon Univ. (2002)] and since many variants were proposed. However, the base algorithm has two variants: the first variant presented in [loc. cit.] and its slightly modified version used afterwards, e.g. in [X. Zhu, Semi-supervised learning with graphs. Pittsburgh, PA: Carnegie Mellon University (PhD Thesis) (2005)]. This paper presents and compares the two algorithms – both theoretically and experimentally – and also tries to make a recommendation which variant to use.

##### MSC:

68T05 | Learning and adaptive systems in artificial intelligence |

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\textit{Z. Bodó} and \textit{L. Csató}, Acta Univ. Sapientiae, Inform. 7, No. 1, 18--30 (2015; Zbl 1334.68178)

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##### References:

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