# Application of Label Correlation in Multi-Label Classification: A Survey
## 2 Overview of Label Correlation
### Introduction to Label Correlation  
Label correlations refers to the relationship between labels in MLC. In MLC, one of the keys to improving classification performance is the effective utilization of the complex correlations among output labels [25; 26; 27]. The quality of model performance often depends on the effective utilization of label correlations. Labels typically exhibit complex relationships, including rich semantic information, hierarchical structures, various intricate associations, and ambiguities. Efficiently leveraging label correlations can assist the model in better learning feature representations, thereby improving feature representation, enhancing prediction accuracy, reducing feature dimensionality and noise impact, boosting model generalization ability and training efficiency, and optimizing model complexity. In practical applications, the specific impact of label correlations on model performance can be empirically validated through experiments, and adjustments and optimizations can be made based on actual requirements. Most current methods, in the process of exploring label correlations, usually abstract the complex relationships between labels further and reduce them to a \"co-occurrence\" relationship. When two label objects frequently appear together, they are considered to have a strong positive association. Previous methods mostly use co-occurrence information between labels [28; 29] to model the degree of correlation between different labels. If a model algorithm can effectively learn the potentially valuable information present among various labels, it will be easier to distinguish between such \"co-occurring\" labels. For instance, [PERSON] et al. [30] proposed a novel multi-label analysis method (CXR\(\times\)MLAGCPL) for analyzing chest X-ray (CXR) images, which effectively learns the valuable information among various pathologies and aids in distinguishing co-occurring pathologies. [PERSON] et al. [31] noted that although graph-based methods perform well in MLC tasks, they only calculate the co-occurrence probabilities between label pairs and overlook higher-order label associations. To address this issue, they proposed a MLC method called DGCL, which aims to leverage both second-order and higher-order label associations to generate label-specific features. Additionally, this method updates the two types of label semantic relationships through a contrastive learning objective.  
However, in real-world scenarios, labels are not just simply co-occurring in the same entity or data; they may also exhibit mutually exclusive relationships. This mutually exclusive relationship means that certain labels are logically or practically unlikely to appear together in the same entity or scenario. Therefore, according to the nature of the interrelationships between labels, label co-occurrence relationships can be classified as positive label correlations, while mutually exclusive relationships can be classified as negative label correlations.  
Furthermore, some research works classify label correlations based on their applicable scope, i.e., the granularity of sharing. Specifically, when label correlations apply to the entire data sample set, they are referred to as global label correlations; when they apply only to local data subsets, they are referred to as local label correlations. As shown in Figure 1, the following section will classify label correlations based on the two dimensions of the nature of label correlations and the granularity of sharing, and introduce each type of label correlations.