News Release

Active label distribution learning via kernel maximum mean discrepancy

Peer-Reviewed Publication

Higher Education Press

Label Distribution Learning (LDL) is a new learning paradigm to deal with label ambiguity and many researches have achieved the prominent performances. Compared with traditional supervised learning scenarios, the annotation with label distribution is more expensive. Direct use of existing Active Learning (AL) approaches, which aim to reduce the annotation cost in traditional learning, may lead to the degradation of their performance.

To solve the problems, a research team led by Tingjin LUO published their new research on 15 Aug 2023 in Frontiers of Computer Science co-published by Higher Education Press and Springer Nature.

The team proposed the Active Label Distribution Learning via Kernel Maximum Mean Discrepancy (ALDL-kMMD) method. Compared with the traditional AL methods, the effectiveness of the proposed method is validated with extensive experiments on the real-world datasets, that the performance of our ALDL-kMMD outperforms others.

In the research, they tackle this crucial but rarely studied problem. ALDL-kMMD captures the structural information of both data and label, extracts the most representative instances from the unlabeled ones by incorporating the nonlinear model and marginal probability distribution matching. Besides, it is also able to markedly decrease the amount of queried unlabeled instances. Meanwhile, an effective solution is proposed for the original optimization problem of ALDL-kMMD by constructing auxiliary variables. The effectiveness of our method is validated with experiments on the real-world datasets.

Future work can focus on applying the proposed active learning method into deep learning structures and designing a novel deep active learning method to reduce the dependence of label information.

DOI: 10.1007/s11704-022-1624-5


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