论文标题
多标签特征选择的随机歧管采样和关节稀疏正则化
Random Manifold Sampling and Joint Sparse Regularization for Multi-label Feature Selection
论文作者
论文摘要
多标签学习通常用于挖掘功能和标签之间的相关性，并且功能选择可以通过少量功能保留尽可能多的信息。 $ \ ell_ {2,1} $正则化方法可以得到稀疏的系数矩阵，但无法有效地解决多重共线性问题。本文提出的模型可以通过求解$ \ ell_ {2,1} $和$ \ ell_ {f} $正则化的联合约束优化问题来获得最相关的少数功能。在流动正则化中，我们基于共同信息矩阵实施随机步行策略，并获得了非常强大的邻域图，并获得了一个非常强大的实验。集合表明该提出的方法优于其他方法。
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.
