Partial label learning (PLL) is a typical weakly supervised learning problem,
where each training example is associated with a set of candidate labels among
which only one is true. Most existing PLL approaches assume that the incorrect
labels in each training example are randomly picked as the candidate labels.
However, this assumption is not realistic since the candidate labels are always
instance-dependent. In this paper, we consider instance-dependent PLL and
assume that each example is associated with a latent label distribution
constituted by the real number of each label, representing the degree to each
label describing the feature. The incorrect label with a high degree is more
likely to be annotated as the candidate label. Therefore, the latent label
distribution is the essential labeling information in partially labeled
examples and worth being leveraged for predictive model training. Motivated by
this consideration, we propose a novel PLL method that recovers the label
distribution as a label enhancement (LE) process and trains the predictive
model iteratively in every epoch. Specifically, we assume the true posterior
density of the latent label distribution takes on the variational approximate
Dirichlet density parameterized by an inference model. Then the evidence lower
bound is deduced for optimizing the inference model and the label distributions
generated from the variational posterior are utilized for training the
predictive model. Experiments on benchmark and real-world datasets validate the
effectiveness of the proposed method. Source code is available at
this https URL

本文提出了一种基于标签增强的偏标签学习方法，该方法针对实例相关性的标签进行了建模，并将标签分布作为标签增强过程的核心信息，以较高的精度训练了预测模型。