Semi-supervised label distribution learning via projection graph embedding

Label distribution learning (LDL) is a new machine learning paradigm that addresses label ambiguity by emphasizing the relevance of each label to a particular instance. In supervised learning, many LDL algorithms have been proposed, which often require a large amount of well-annotated training data to achieve good performance. However, annotating a label distribution is more complicated and expensive than annotating a single label or multiple labels with logical values of 0 and 1. Thus, we propose a projection graph embedding algorithm for semi-supervised label distribution learning (PGE-SLDL). Specifically, we seek a potential space by orthogonal neighborhood preserving projections, named capture space. This capture space is used to select more valuable features and construct a graph that contains more accurate data structure information. We utilize the sample correlation information contained between graph nodes to recover the unknown label distribution. In addition, compared with fixed graphs in traditional semi-supervised learning, we carry out projection and graph construction simultaneously to obtain a self-updating projection graph, which is more helpful to learn label distribution. The experimental results validate the effectiveness of the proposed algorithm. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

Label distribution learning (LDL) is a new paradigm in machine learning that addresses label ambiguity by emphasizing the relevance of each label to a particular instance. We propose a projection graph embedding algorithm for semi-supervised label distribution learning (PGE-SLDL), which aims to select valuable features, construct an accurate graph, and recover unknown label distributions. The self-updating projection graph is more effective in learning label distribution compared to traditional fixed graphs in semi-supervised learning.