Mar 7, 2024 · We propose a novel multi-label feature selection method named LRDG that explores latent representation learning and dynamic graph constraints.
Jul 9, 2024 · Highlights · LRDG achieves full constraints on pseudo-label. · A pseudo-label based dynamic graph is designed to constrain the feature weights.
Graph embedding maps a graph into a convenient vector-space representation for graph analysis and machine learning applications. Many graph embedding methods ...
Base on this knowledge, we propose a novel multi-label feature selection method named LRDG that explores latent representation learning and dynamic graph ...
We proposed a multi-label feature selection method with latent representation and dynamic graph constraints. Firstly, the proposed method used the latent ...
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Multi-Label Feature Selection Based on Latent Representation Learning and Dynamic Graph Constraints. https://doi.org/10.2139/ssrn.4578837. Journal: 2023.
This paper proposes a non-negative multi-label feature selection (NMDG) with dynamic graph constraints to address this issue. In the NMDG model, the original ...
Oct 22, 2024 · To verify the effectiveness of SLCLR for multi-label feature selection, we acquired thirteen datasets from the Mulan Library [38] as ...
Apr 17, 2024 · A new semi-supervised multi-label feature selection method is proposed by combining the sparse regular term, latent representation, and dynamic ...