In the last decade, embedded multi-label feature selection methods, incorporating the search for feature subsets into model optimization, have attracted considerable attention in accurately evaluating the importance of features in multi-label classification tasks. Nevertheless, the state-of-the-art embedded multi-label feature selection algorithms based on least square regression usually cannot preserve sufficient discriminative information in multi-label data. To tackle the aforementioned challenge, a novel embedded multi-label feature selection method, termed global redundancy and relevance optimization in orthogonal regression (GRROOR), is proposed to facilitate the multi-label feature selection. The method employs orthogonal regression with feature weighting to retain sufficient statistical and structural information related to local label correlations of the multi-label data in the feature learning process. Additionally, both global feature redundancy and global label relevancy information have been considered in the orthogonal regression model, which could contribute to the search for discriminative and non-redundant feature subsets in the multi-label data. The cost function of GRROOR is an unbalanced orthogonal Procrustes problem on the Stiefel manifold. A simple yet effective scheme is utilized to obtain an optimal solution. Extensive experimental results on ten multi-label data sets demonstrate the effectiveness of GRROOR.

综合考虑了嵌入式多标签特征选择中的全局冗余和相关性优化，提出了一种名为GRROOR的新方法，以在特征学习过程中保留与多标签数据的局部标签相关性相关的足够的统计和结构信息，在寻找多标签数据中的具有辨别力和非冗余的特征子集方面作出贡献，通过正交回归和特征加权等方法实现。在十个多标签数据集上的广泛实验结果证明了GRROOR的有效性。

嵌入式多标签特征选择的正交回归