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Multi-label classification via learning a unified object-label graph with sparse representation

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Abstract

Automatic annotation is an essential technique for effectively handling and organizing Web objects (e.g., Web pages), which have experienced an unprecedented growth over the last few years. Automatic annotation is usually formulated as a multi-label classification problem. Unfortunately, labeled data are often time-consuming and expensive to obtain. Web data also accommodate much richer feature space. This calls for new semi-supervised approaches that are less demanding on labeled data to be effective in classification. In this paper, we propose a graph-based semi-supervised learning approach that leverages random walks and ℓ 1 sparse reconstruction on a mixed object-label graph with both attribute and structure information for effective multi-label classification. The mixed graph contains an object-affinity subgraph, a label-correlation subgraph, and object-label edges with adaptive weight assignments indicating the assignment relationships. The object-affinity subgraph is constructed using ℓ 1 sparse graph reconstruction with extracted structural meta-text, while the label-correlation subgraph captures pairwise correlations among labels via linear combination of their co-occurrence similarity and kernel-based similarity. A random walk with adaptive weight assignment is then performed on the constructed mixed graph to infer probabilistic assignment relationships between labels and objects. Extensive experiments on real Yahoo! Web datasets demonstrate the effectiveness of our approach.

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Notes

  1. http://sparselab.stanford.edu

  2. http://users.ece.gatech.edu/~justin/l1magic/

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Acknowledgements

Quan Z. Sheng’s work has been partially supported by Australian Research Council (ARC) Discovery Grant DP140100104 and Future Fellowship Project FT140101247. Xue Li’s work has been partially supported by Australian Research Council (ARC) Discovery Grant DP130104614. The authors would like to thank the anonymous reviewers for their valuable feedback on this work.

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Authors and Affiliations

  1. School of Computer Science, The University of Adelaide, Adelaide, SA, 5005, Australia

    Lina Yao & Quan Z. Sheng

  2. Department of Computer Science, Texas State University, San Marcos, TX, 78666-4616, USA

    Anne H. H. Ngu & Byron J. Gao

  3. School of Information Technology and Electrical Engineering, The University of Queensland, Queensland, QLD, 4072, Australia

    Xue Li & Sen Wang

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  1. Lina Yao
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Correspondence to Lina Yao.

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Yao, L., Sheng, Q.Z., Ngu, A.H.H. et al. Multi-label classification via learning a unified object-label graph with sparse representation. World Wide Web 19, 1125–1149 (2016). https://doi.org/10.1007/s11280-015-0376-7

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