Partial multi-label feature selection with feature noise

Abstract

As the dimensionality of multi-label data continues to increase, feature selection has become increasingly prevalent in multi-label learning, serving as an efficient and interpretable means of dimensionality reduction. However, existing multi-label feature selection algorithms often assume data to be noise-free, which cannot hold in real-world applications where feature and label noise are frequently encountered. Therefore, we propose a novel partial multi-label feature selection algorithm, which aims to effectively select an optimal subset of features in the environment plagued by feature noise and partial multi-label. Specifically, we first propose a robust label enhancement model to diminish noise interference and enrich the semantic information of labels. Subsequently, a sparse reconstruction is utilized to learn the instance relevance information and then applied to the smoothness assumption to obtain more accurate label distributions. Additionally, we employ the ${\ell }_{2,1}$-norm to eliminate irrelevant features and constrain the model complexity. Finally, the above processing is optimized end-to-end within a unified objective function. Experimental results demonstrate that our algorithm outperforms several state-of-the-art feature selection methods across 15 datasets.