# Application of Label Correlation in Multi-Label Classification: A Survey
## 5 Applications of Label Correlation to MLC Subproblems
In this context, all samples are associated with a set of candidate labels, but only some of these labels are accurate. Research has begun to focus on how label correlation can improve classification performance on ambiguous data [68]. To resolve the ambiguity of candidate labels, [PERSON] et al. [86] proposed a method called Learning Shared and Non-redundant Label-specific Features (LSNRLS). Meanwhile, [PERSON] et al. [87] introduced a disambiguation method for PML (PML-ND), which aims to enhance label accuracy by utilizing negative labels and noise information. Table 3 illustrates the application of label correlations in various MLC subproblems. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Model/ Method** & **Year** & **Brief Introduction to Solve** \\ \hline \multirow{2}{*}{LCML [80]} & \multirow{2}{*}{2014} & A probabilistic model utilizing a covariance matrix to model label correlations is proposed, along with a normative method for handling missing labels. \\ \hline \multirow{2}{*}{GLOCAL [66]} & \multirow{2}{*}{2017} & The method of jointly modeling global and local label correlations is proposed for the first time, utilizing low-rank decomposition of the observed label matrix to obtain more compact and abstract latent label representations. This approach aims to effectively recover missing labels. \\ \hline \multirow{2}{*}{LSLC-ML [81]} & \multirow{2}{*}{2020} & This study utilizes both positive and negative label correlations to address missing labels and develops a MLC model through label-specific feature selection. The model distinctly combines the management of positive and negative label correlations, label-specific feature selection, and the treatment of missing labels. \\ \hline \multirow{2}{*}{LCFM [71]} & \multirow{2}{*}{2024} & A novel method for missing label imputation in MLC tasks, which enhances label correlations through the learning of global and local label labels \\ \hline \multirow{2}{*}{HLESE [22]} & \multirow{2}{*}{2023} & A method for augmenting the label set of each document in the training dataset by including any missing categories along the hierarchical path, thereby creating a more cohesive training dataset. \\ \hline \multirow{2}{*}{IMLGL [72]} & \multirow{2}{*}{2024} & A machine learning model for addressing imbalanced and missing multi-label data, which enhances the model’s generalization performance through the use of global and local structural information. \\ \hline \multirow{2}{*}{LF-LPLC [76]} & \multirow{2}{*}{2018} & Combining label-specific features with local pairwise label correlations for MLC. \\ \hline \multirow{2}{*}{CLSF [82]} & \multirow{2}{*}{2020} & It is assumed that each label possesses specific features, and the higher the redundancy of these features between two labels, the stronger their correlation. Based on this assumption, both local and global label features correlations are calculated and applied to the feature selection process for multi-label data. \\ \hline \multirow{2}{*}{MLSF [54]} & \multirow{2}{*}{2016} & By employing spectral clustering techniques, label and instance space information is utilized to organize labels into meta-label sets, grouping highly correlated labels together. Following this, label-specific features are selected for each meta-label set. \\ \hline \multirow{2}{*}{LFGLC [70]} & \multirow{2}{*}{2023} & This study combines global and local label correlations to extract features unique to each label. It evaluates these correlations by analyzing label co-occurrence and neighborhood information, which are then integrated into a linear regression model with an \(l_{1}\) norm to identify the specific feature associated with each label. \\ \hline \end{tabular}
\end{table} 
Table 3: Applications of label correlations to other MLC subproblems.