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- W4362635112 abstract "Multi-label learning is usually used to mine the correlation between features and labels, and feature selection can retain as much information as possible through a small number of features. ℓ2,1 regularization method can get sparse coefficient matrix, but it can not solve multicollinearity problem effectively The model proposed in this paper can obtain the most relevant few features by solving the joint constrained optimization problems of ℓ2,1 and ℓF regularization. In manifold regularization, we implement random walk strategy based on joint information matrix, and get a highly robust neighborhood graph. In addition, we gave the algorithm for solving the model and proved its convergence. Comparative experiments on real-world data sets show that the proposed method outperforms other methods." @default.
- W4362635112 created "2023-04-07" @default.
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- W4362635112 date "2023-05-01" @default.
- W4362635112 modified "2023-09-23" @default.
- W4362635112 title "Random Manifold Sampling and Joint Sparse Regularization for Multi-Label Feature Selection" @default.
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- W4362635112 doi "https://doi.org/10.1016/j.bdr.2023.100383" @default.
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