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Variational Label Enhancement
Ning Xu · Jun Shu · Yun-Peng Liu · Xin Geng

Wed Jul 15 05:00 AM -- 05:45 AM & Wed Jul 15 08:00 PM -- 08:45 PM (PDT) @ None #None

Label distribution covers a certain number of labels, representing the degree to which each label describes the instance. The learning process on the instances labeled by label distributions is called label distribution learning (LDL). Unfortunately, many training sets only contain simple logical labels rather than label distributions due to the difficulty of obtaining the label distributions directly. To solve this problem, we consider the label distributions as the latent vectors and infer the label distributions from the logical labels in the training datasets by using variational inference. After that, we induce a predictive model to train the label distribution data by employing the multi-output regression technique. The recovery experiment on thirteen real-world LDL datasets and the predictive experiment on ten multi-label learning datasets validate the advantage of our approach over the state-of-the-art approaches.

Author Information

Ning Xu (Southeast University)
Jun Shu (Xi'an Jiaotong University)
Yun-Peng Liu (Southeast University)
Xin Geng (Southeast University)

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