With increasing data volumes, the bottleneck in obtaining data for training a given learning task is the cost of manually labeling instances within the data. To alleviate this issue, various reduced label settings have been considered including semi-supervised learning, partial- or incomplete-label learning, multiple-instance learning, and active learning. Here, we focus on multiple-instance multiple-label learning with missing bag labels. Little research has been done for this challenging yet potentially powerful variant of incomplete supervision learning. We introduce a novel discriminative probabilistic model for missing labels in multiple-instance multiple-label learning. To address inference challenges, we introduce an efficient implementation of the EM algorithm for the model. Additionally, we consider an alternative inference approach that relies on maximizing the label-wise marginal likelihood of the proposed model instead of the joint likelihood. Numerical experiments on benchmark datasets illustrate the robustness of the proposed approach. In particular, comparison to state-of-the-art methods shows that our approach introduces a significantly smaller decrease in performance when the proportion of missing labels is increased.

中文翻译：

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不完整标签多实例多标签学习


随着数据量的增加，获取用于训练给定学习任务的数据的瓶颈是手动标记数据中的实例的成本。为了缓解这个问题，人们考虑了各种减少标签设置，包括半监督学习、部分或不完整标签学习、多实例学习和主动学习。在这里，我们专注于缺少袋子标签的多实例多标签学习。对于这种具有挑战性但潜在强大的不完全监督学习变体，人们进行的研究还很少。我们引入了一种新颖的判别概率模型，用于处理多实例多标签学习中丢失的标签。为了解决推理挑战，我们为模型引入了 EM 算法的有效实现。此外，我们考虑另一种推理方法，该方法依赖于最大化所提出模型的标签边际似然而不是联合似然。基准数据集的数值实验说明了所提出方法的鲁棒性。特别是，与最先进的方法相比表明，当丢失标签的比例增加时，我们的方法带来的性能下降明显更小。