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Label Correlation Propagation for Semi-supervised Multi-label Learning

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10597)

Abstract

Many real world machine learning tasks suffer from the problem of scarce labeled data. In multi-label learning, each instance is associated with more than one label as in semantic scene understanding, text categorization and bio-informatics. Semi-supervised multi-label learning has attracted recent interest as gathering labeled data is both expensive and requires manual effort. Further, many of the labels have semantic correlation which manifests as co-occurrence and this information can be used to build effective classifiers in the multi-label scenario. In this paper, we propose two different graph based transductive methods, namely, the label correlation propagation and the k-nearest neighbors based label correlation propagation. Extensive experimentation on real-world datasets demonstrates the efficacy of the proposed methods and the importance of using the label correlation information in semi-supervised multi-label learning.

Keywords

Semi-supervised learning Multi-label learning Graph based learning 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, Indian Institute of Technology MadrasChennaiIndia

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