In the domain of multi-view multi-label (MVML) learning, features are distributed across various views, each offering multiple semantic representations. While existing approaches aim to balance commonality and complementarity within the view ...

Fake news spreads at unprecedented speeds through online social media, raising many concerns and negative impacts on a variety of domains. To control this issue, Fake News Detection (FND) naturally becomes the chief task while multimodal FND has ...

• We describe the neutralization effect problem of previous multimodal FND methods.
• We propose a new model MINER-UVS with the PU learning and feature fusion techniques.
• Extensive experiments are conducted to indicate the ...

In multi-view multi-label learning (MVML), the accuracy of feature weights is pivotal for establishing feature order. However, conventional MVML methods often struggle with integrating distinct information from multiple views effectively, leading ...

Multi-label feature selection involves the selection of informative features in high-dimensional data sets based on the relationships among different variables. However, large-scale data sets often contain unknown labels that hold latent ...

Prevalent multi-label feature selection (MLFS) approaches to obtain the most suitable feature subset by dealing with two issues, namely sparsity and redundancy. In this paper, we design an efficient Elastic net based high Sparse personalized and ...

• ESRFS can ensure high-sparse personalized features for each label and select low-redundancy shared features for all labels.
• ESRFS can overcome the limitations of LASSO and L2,1-norms in multi-label problems.
• ESRFS is introduced ...

Multi-view and multi-label data offer a comprehensive perspective for learning models, but dimensionality poses a challenge for feature selection. Existing methods based on information theory solely focus on feature contribution to the entire ...

Feature selection is an effective way to improve the model learning performance while being a challenging problem in the Partial Multi-label Learning (PML). Different from multi-label learning, PML is closer to reality, which means the annotators ...

• Hard negatives can relieve the vanishing gradient problem
• Mixing distribution ...

Recently, unsupervised graph representation learning has attracted considerable attention through effectively encoding graph-structured data without semantic annotations. To accelerate its training, noise contrastive estimation (NCE) ...

• SLMDS uses the self-expression model to preserve global label correlations.
• A ...

Recent years, joint feature selection and multi-label learning have received extensive attention as an open problem. However, there exist three general issues in previous multi-label feature selection methods. First of all, existing ...

• Clearing up two basic types of probability distribution assumption.
• Concluding ...

The approximation of low-order information-theoretic terms for feature selection approaches has achieved success in addressing high-dimensional multi-label data. However, three critical issues exist in such type of approaches: (1) ...

• A regularization norm named robust flexible sparse regularization (RFSR) is designed.

Multi-label feature selection is an efficient technique to deal with the high dimensional multi-label data by selecting the optimal feature subset. Existing researches have demonstrated that l 1-norm and l 2 , 1-norm are promising ...

Feature selection has attracted considerable attention due to the wide application of multi-label learning. However, previous methods do not fully consider the relationship between feature sets and label sets but devote attention to either of ...${}_{\mathrm{}\mathrm{}}$

Numerous high-dimension multi-label data are produced, leading to the imperative need to design excellent multi-label feature selection methods. It is of paramount importance to exploit label correlations in previous methods. However, ...

Recent years has witnessed urgent needs for addressing the curse of dimensionality regarding multi-label data, which attracts wide attention for feature selection. Feature relevance terms are often constructed depending on the amount ...

Multi-label feature selection attracts considerable attention from multi-label learning. Information theory-based multi-label feature selection methods intend to select the most informative features and reduce the uncertain amount of information ...

In multi-label learning, feature selection is a topical issue for addressing high-dimension data. However, most of existing methods adopt imperfect labels to perform feature selection. Although some graph-based multi-label feature ...

• We distinguish the different effects of label relationship on feature selection.

Multi-label feature selection is an efficient technique to alleviate the high dimensionality for multi-label learning. Existing multi-label feature selection methods based on information theory either deal with labels individually or ...

Feature selection is an important preprocessing operation in the fields of machine learning and data mining. Information theory is widely used in feature selection methods because it can measure linear and nonlinear correlations among ...

• We design a new feature relevancy term.
• The new feature relevance term includes ...

Multi-label feature selection is a critical dimension reduction technique in multi-label learning. In conventional multi-label feature selection methods based on information theory, feature relevance is evaluated by mutual information between ...

• We categorize labels into two groups: independent labels and dependent labels.
• ...

Multi-label feature selection plays an important role in pattern recognition, which can improve multi-label classification performance. In traditional multi-label feature selection methods based on information theory, feature relevance ...