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Arxiv
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Graph Random Walk with Feature-Label Space Alignment: A Multi-Label Feature Selection Method
- The article introduces a new method for feature selection in multi-label datasets called Graph Random Walk with Feature-Label Space Alignment.
- The method addresses the complexity arising from the implicit associations between features and labels in datasets with high feature dimensions.
- It utilizes a random walk graph to capture nonlinear and indirect associations between features and labels, improving over traditional linear decomposition methods.
- Experiments on benchmark and representative datasets show the effectiveness of the proposed method in accurately selecting features in multi-label datasets.
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