Broad Learning System (BLS) is an emerging network paradigm that has received considerable attention in the regression and classification fields. However, there are two deficiencies which seriously hinder its deployment in real applications. The first one is the internal correlations among samples are not fully considered in the modeling process. Second, the strict binary label matrix utilized in BLS provides little freedom for classification. In this paper, to address the above issues, we propose to impose group-sparsity constraints on the class-specific transformed features and label error terms, respectively. The effect is not only the more appropriate margins between data can be preserved, but also the learnt label space can be flexible for recognition. As a result, the obtained projection matrix can show more vital discriminative ability. Further, we employ the alternating direction method of multipliers to solve the resulting optimization problem. Extensive experiments and analysis on diverse benchmark databases are carried out to confirm our proposed model’s superiority in comparison with other competing classification methods.

广泛学习系统（BLS）是一种新兴的网络范式，在回归和分类领域受到了相当大的关注。然而，有两个缺陷严重阻碍了其在实际应用中的部署。第一个是建模过程中没有充分考虑样本之间的内部相关性。其次，BLS 中使用的严格二进制标签矩阵为分类提供了很小的自由度。在本文中，为了解决上述问题，我们建议分别对特定于类的转换特征和标签误差项施加组稀疏约束。效果不仅是可以保留更合适的数据之间的边距，而且学习到的标签空间可以灵活用于识别。结果，获得的投影矩阵可以显示出更重要的判别能力。此外，我们采用乘法器的交替方向方法来解决由此产生的优化问题。对各种基准数据库进行了广泛的实验和分析，以确认我们提出的模型与其他竞争分类方法相比的优越性。