Multi-label Active Learning Based on Maximum Correntropy Criterion: Towards Robust and Discriminative Labeling

  • Zengmao Wang
  • Bo Du
  • Lefei Zhang
  • Liangpei Zhang
  • Meng Fang
  • Dacheng Tao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9907)


Multi-label learning is a challenging problem in computer vision field. In this paper, we propose a novel active learning approach to reduce the annotation costs greatly for multi-label classification. State-of-the-art active learning methods either annotate all the relevant samples without diagnosing discriminative information in the labels or annotate only limited discriminative samples manually, that has weak immunity for the outlier labels. To overcome these problems, we propose a multi-label active learning method based on Maximum Correntropy Criterion (MCC) by merging uncertainty and representativeness. We use the the labels of labeled data and the prediction labels of unknown data to enhance the uncertainty and representativeness measurement by merging strategy, and use the MCC to alleviate the influence of outlier labels for discriminative labeling. Experiments on several challenging benchmark multi-label datasets show the superior performance of our proposed method to the state-of-the-art methods.


Multi-label learning Active learning Correntropy Robust 



This work was supported in part by the National Basic Research Program of China (973 Program) under Grant 2012CB719905, the National Natural Science Foundation of China under Grants 61471274, 41431175, 61401317, U1536204, 60473023, 61302111, and the Australian Research Council Projects DP-140102164, FT-130101457, and LE140100061.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Zengmao Wang
    • 1
  • Bo Du
    • 1
  • Lefei Zhang
    • 1
  • Liangpei Zhang
    • 2
  • Meng Fang
    • 3
  • Dacheng Tao
    • 4
  1. 1.State Key Laboratory of Software Engineering, School of ComputerWuhan UniversityWuhanChina
  2. 2.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote SensingWuhan UniversityWuhanChina
  3. 3.Department of Computing and Information SystemsUniversity of MelbourneParkvilleAustralia
  4. 4.QCIS and FEITUniversity of Technology SydneySydneyAustralia

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