Label Distribution Learning from Logical Label
This addresses the annotation cost issue for researchers in machine learning, though it is incremental as it builds on existing label distribution learning and label enhancement approaches.
The paper tackles the problem of costly label distribution annotation by proposing a method to learn a label distribution learning model directly from logical labels, unifying label enhancement and label distribution learning into a joint model. Experiments show it produces more accurate label distributions than state-of-the-art methods.
Label distribution learning (LDL) is an effective method to predict the label description degree (a.k.a. label distribution) of a sample. However, annotating label distribution (LD) for training samples is extremely costly. So recent studies often first use label enhancement (LE) to generate the estimated label distribution from the logical label and then apply external LDL algorithms on the recovered label distribution to predict the label distribution for unseen samples. But this step-wise manner overlooks the possible connections between LE and LDL. Moreover, the existing LE approaches may assign some description degrees to invalid labels. To solve the above problems, we propose a novel method to learn an LDL model directly from the logical label, which unifies LE and LDL into a joint model, and avoids the drawbacks of the previous LE methods. Extensive experiments on various datasets prove that the proposed approach can construct a reliable LDL model directly from the logical label, and produce more accurate label distribution than the state-of-the-art LE methods.