  Multi-label learning draws great interests in many real world applications.
It is a highly costly task to assign many labels by the oracle for one
instance. Meanwhile, it is also hard to build a good model without diagnosing
discriminative labels. Can we reduce the label costs and improve the ability to
train a good model for multi-label learning simultaneously?
  Active learning addresses the less training samples problem by querying the
most valuable samples to achieve a better performance with little costs. In
multi-label active learning, some researches have been done for querying the
relevant labels with less training samples or querying all labels without
diagnosing the discriminative information. They all cannot effectively handle
the outlier labels for the measurement of uncertainty. Since Maximum
Correntropy Criterion (MCC) provides a robust analysis for outliers in many
machine learning and data mining algorithms, in this paper, we derive a robust
multi-label active learning algorithm based on MCC by merging uncertainty and
representativeness, and propose an efficient alternating optimization method to
solve it. With MCC, our method can eliminate the influence of outlier labels
that are not discriminative to measure the uncertainty. To make further
improvement on the ability of information measurement, we merge uncertainty and
representativeness with the prediction labels of unknown data. It can not only
enhance the uncertainty but also improve the similarity measurement of
multi-label data with labels information. Experiments on benchmark multi-label
data sets have shown a superior performance than the state-of-the-art methods.
