With the increasing scale of obtained multi-view data, how to deal with large-scale multi-view data quickly and efficiently is a significant problem. In this paper, a novel supervised multi-view feature selection method based on locally sparse regularization and block computing is proposed to solve the problem. Specifically, the multi-view dataset is firstly divided into sub-blocks according to classes and views. Then with the aid of the Alternating Direction Method of Multipliers (ADMM), a sharing sub-model is proposed to perform feature selection on each class by integrating each view’s locally sparse regularizers and shared loss that makes all views share a common penalty and regresses samples to their labels. Finally, all the sharing sub-models are fused to form the final general additive feature selection model, in which each sub-block adjusts its corresponding variables to perform block-based feature selection. In the optimization process, the proposed model can be decomposed into multiple separate subproblems, and an efficient optimization algorithm is proposed to solve them quickly. The comparison experiments with several state-of-the-art feature selection methods show that the proposed method is superior in classification accuracy and training speed.

随着获取的多视图数据规模越来越大，如何快速高效地处理大规模多视图数据是一个重大问题。在本文中，提出了一种基于局部稀疏正则化和块计算的新型监督多视图特征选择方法来解决该问题。具体来说，首先将多视图数据集根据类别和视图划分为子块。然后在乘法器交替方向法（ADMM）的帮助下，提出了一个共享子模型，通过集成每个视图的局部稀疏正则化器和共享损失来对每个类进行特征选择，使所有视图共享公共惩罚并回归样本到他们的标签。最后，所有共享子模型融合形成最终的通用加性特征选择模型，其中每个子块调整其相应的变量以执行基于块的特征选择。在优化过程中，所提出的模型可以分解为多个独立的子问题，并提出一种高效的优化算法来快速解决它们。与几种最先进的特征选择方法的对比实验表明，所提出的方法在分类准确率和训练速度方面具有优越性。