<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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 [8] and since many variants were proposed. However, the base algorithm has two variants: the first variant presented in [8] and its slightly modified version used afterwards, e.g. in [7]. This paper presents and compares the two algorithms—both theoretically and experimentally—and also tries to make a recommendation which variant to use.</p></abstract>

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 [8] and since many variants were proposed. However, the base algorithm has two variants: the first variant presented in [8] and its slightly modified version used afterwards, e.g. in [7]. This paper presents and compares the two algorithms—both theoretically and experimentally—and also tries to make a recommendation which variant to use.

A note on label propagation for semi-supervised learning

Acta Universitatis Sapientiae, Informatica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"A note on label propagation for semi-supervised learning"}

Semi-supervised learning has become an important and thoroughly studied subdomain of machine learning in the past few years, because gathering large unlabeled...

A note on label propagation for semi-supervised learning

Published Online: Jun 08, 2015

Page range: 18 - 30

Received: Aug 11, 2014

DOI: https://doi.org/10.1515/ausi-2015-0010

© Zalán Bodó et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.