Multilabel Feature Selection With Constrained Latent Structure Shared Term.

High-dimensional multilabel data have increasingly emerged in many application areas, suffering from two noteworthy issues: instances with high-dimensional features and large-scale labels. Multilabel feature selection methods are widely studied to address the issues. Previous multilabel feature selection methods focus on exploring label correlations to guide the feature selection process, ignoring the impact of latent feature structure on label correlations. In addition, one encouraging property regarding correlations between features and labels is that similar features intend to share similar labels. To this end, a latent structure shared (LSS) term is designed, which shares and preserves both latent feature structure and latent label structure. Furthermore, we employ the graph regularization technique to guarantee the consistency between original feature space and latent feature structure space. Finally, we derive the shared latent feature and label structure feature selection (SSFS) method based on the constrained LSS term, and then, an effective optimization scheme with provable convergence is proposed to solve the SSFS method. Better experimental results on benchmark datasets are achieved in terms of multiple evaluation criteria.

Graph Multihead Attention Pooling with Self-Supervised Learning.

Graph neural networks (GNNs), which work with graph-structured data, have attracted considerable attention and achieved promising performance on graph-related tasks. While the majority of existing GNN methods focus on the convolutional operation for encoding the node representations, the graph pooling operation, which maps the set of nodes into a coarsened graph, is crucial for graph-level tasks. We argue that a well-defined graph pooling operation should avoid the information loss of the local node features and global graph structure. In this paper, we propose a hierarchical graph pooling method based on the multihead attention mechanism, namely GMAPS, which compresses both node features and graph structure into the coarsened graph. Specifically, a multihead attention mechanism is adopted to arrange nodes into a coarsened graph based on their features and structural dependencies between nodes. In addition, to enhance the expressiveness of the cluster representations, a self-supervised mechanism is introduced to maximize the mutual information between the cluster representations and the global representation of the hierarchical graph. Our experimental results show that the proposed GMAPS obtains significant and consistent performance improvements compared with state-of-the-art baselines on six benchmarks from the biological and social domains of graph classification and reconstruction tasks.

Multi-Label Feature Selection Based on High-Order Label Correlation Assumption.

Multi-label data often involve features with high dimensionality and complicated label correlations, resulting in a great challenge for multi-label learning. Feature selection plays an important role in multi-label learning to address multi-label data. Exploring label correlations is crucial for multi-label feature selection. Previous information-theoretical-based methods employ the strategy of cumulative summation approximation to evaluate candidate features, which merely considers low-order label correlations. In fact, there exist high-order label correlations in label set, labels naturally cluster into several groups, similar labels intend to cluster into the same group, different labels belong to different groups. However, the strategy of cumulative summation approximation tends to select the features related to the groups containing more labels while ignoring the classification information of groups containing less labels. Therefore, many features related to similar labels are selected, which leads to poor classification performance. To this end, Max-Correlation term considering high-order label correlations is proposed. Additionally, we combine the Max-Correlation term with feature redundancy term to ensure that selected features are relevant to different label groups. Finally, a new method named Multi-label Feature Selection considering Max-Correlation (MCMFS) is proposed. Experimental results demonstrate the classification superiority of MCMFS in comparison to eight state-of-the-art multi-label feature selection methods.