IEEE Trans Pattern Anal Mach Intell. 2017 Dec;39(12):2381-2394. doi: 10.1109/TPAMI.2017.2647944. Epub 2017 Jan 5.
Labeling data for classification requires significant human effort. To reduce labeling cost, instead of labeling every instance, a group of instances (bag) is labeled by a single bag label. Computer algorithms are then used to infer the label for each instance in a bag, a process referred to as instance annotation. This task is challenging due to the ambiguity regarding the instance labels. We propose a discriminative probabilistic model for the instance annotation problem and introduce an expectation maximization framework for inference, based on the maximum likelihood approach. For many probabilistic approaches, brute-force computation of the instance label posterior probability given its bag label is exponential in the number of instances in the bag. Our contribution is a dynamic programming method for computing the posterior that is linear in the number of instances. We evaluate our method using both benchmark and real world data sets, in the domain of bird song, image annotation, and activity recognition. In many cases, the proposed framework outperforms, sometimes significantly, the current state-of-the-art MIML learning methods, both in instance label prediction and bag label prediction.
分类的标注数据需要大量的人工投入。为了降低标注成本，不是对每个实例进行标注，而是由单个的标注组（bag）来标注一组实例。然后，计算机算法用于推断 bag 中每个实例的标签，这个过程被称为实例标注。由于实例标签的不明确性，这个任务具有挑战性。我们提出了一种用于实例标注问题的判别概率模型，并基于最大似然方法，引入了一种用于推断的期望最大化框架。对于许多概率方法，给定 bag 标签后计算实例标签后验概率的穷举计算在 bag 中的实例数量上是指数级的。我们的贡献是一种用于计算后验的动态规划方法，其数量与实例数量呈线性关系。我们在鸟叫声、图像标注和活动识别领域的基准数据集和真实数据集上评估了我们的方法。在许多情况下，所提出的框架在实例标签预测和 bag 标签预测方面都优于当前最先进的多实例学习方法，有时甚至显著优于它们。
