Related papers: Random Manifold Sampling and Joint Sparse Regularization for Multi-label Feature Selection

Random Manifold Sampling and Joint Sparse Regularization for Multi-label Feature Selection

URL: http://arxiv.org/abs/2204.06445v3
Date: Thu, 30 Mar 2023 13:00:41 GMT
Title: Random Manifold Sampling and Joint Sparse Regularization for Multi-label Feature Selection
Authors: Haibao Li and Hongzhi Zhai
Abstract summary: The model proposed in this paper can obtain the most relevant few features by solving the joint constrained optimization problems of $ell_2,1$ and $ell_F$ regularization. Comparative experiments on real-world data sets show that the proposed method outperforms other methods.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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. $\ell_{2,1}$ 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 $\ell_{2,1}$ and $\ell_{F}$ regularization.In manifold regularization, we implement random walk strategy based on joint information matrix, and get a highly robust neighborhood graph.In addition, we given 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.

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