# Application of Label Correlation in Multi-Label Classification: A Survey
## 4 Applications of Label Correlation in MLC Methods
### Local MLC Methods Considering Only Positive Label Correlation  
Traditional global MLC methods typically learn and utilize positive label correlations from a global perspective, assuming they are applicable to all sample instances. This approach can have some negative impacts, such as imposing incorrect constraints on certain instances, leading the model to assign many irrelevant or even erroneous labels to instances. To mitigate these negative effects, it is necessary to explore label correlations locally. These methods locally learn label correlations within each data subset, following the basic assumption that dataset instances can be divided into different groups, with instances within each group sharing a subset of label correlations. Therefore, it is necessary to partition the dataset into different groups. How can a dataset be divided into different groups? The ML-LOC method [34] groups the dataset using the k-means clustering algorithm [78] by measuring the similarity between instances in the label space, assuming that instances with similar label vectors typically share the same label correlations. The GD-LDL-SCL method [36] follows a similar premise, focusing on measuring the similarity between instances in the label space, which is generally smaller than the feature space, thus effectively reducing the time required for clustering. In contrast, the GCC method [65] posits that similar instances not only share label correlations but also tend to have similar labels; therefore, it treats the label space as an additional feature in the original feature space and performs clustering in the combined \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Global Methods** & **Local Methods** & **Global-Local Methods** \\ \hline only negative correlations & LELR [73] (2011) & / & \\ \hline both positive and negative correlations & [PERSON] et al. [39] (2014) & LPLC [33] (2016) & \\  & [PERSON] & LF-LPLC [76] (2018) & / \\  & et al. [74] (2015) & [PERSON] et al. [32] (2018) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._feature space. In contrast, the LPLC [33] and LF-LPLC [76] methods identify the k-nearest neighbors of instances in the feature space using Euclidean distances and learn the local correlations of labels within these neighbors. After grouping data based on different assumptions, existing local relation methods employ various strategies to utilize the local label correlations within different groups. For instance, the ML-LOC [34] method constructs a LOC code vector for each instance. In this code vector, the larger the value of an element in the corresponding position, the more the subset of label correlations shared by instances in that group helps the instance. In other words, each instance's LOC code reflects the influence of various label correlations subsets on the instance. The LOC code is then used as an additional feature to the original features, and a classifier is trained based on the extended features, thereby encoding the local impact of label correlations. Inspired by ML-LOC, [PERSON] et al. [36] designed local correlation vectors based on different local samples after clustering, which were used as additional features for each instance, introducing local label correlations in label distribution learning. The GCC method proposed by [PERSON] et al. [65] learns directed label dependency graphs in each cluster after clustering the dataset. The dependency probabilities between two labels are modeled by label co-occurrence, and then a specific classifier chain is constructed based on the instances in each group and the corresponding label dependency graph. In summary, compared to global methods, local methods effectively mitigate the potential negative impacts of global methods by focusing on finer-grained label correlations. Experimental results on multiple datasets indicate that local methods achieve better performance than global methods in certain situations, further demonstrating the significant potential and advantages of local methods in addressing specific problems.