Partial Multi-Label Learning (PML) is an extension of multi-label classification where each training instance is associated with a set of candidate labels that contains both relevant and noisy labels. The presence of high-dimensional feature representations in PML exacerbates learning complexity, thereby heightening the model’s sensitivity to noise and irrelevant information. To tackle this issue, we propose a Partial Multi-Label Feature Selection method based on Matrix Elastic-Net (PMLFS-MEN). Firstly, we employ a low-rank and sparse decomposition to separate the candidate label matrix into a low-rank ground-truth label matrix and a sparse noise label matrix. Subsequently, we incorporate matrix elastic-net regularization, where the nuclear norm is regarded as the \(\ell _{1}\) -norm of the singular values of the ground-truth matrix and the Frobenius norm as its \(\ell _{2}\) -norm, thereby encouraging a balanced low-rank structure and improving stability. Moreover, a non-convex \(\ell _{2,1-2}\) -norm is introduced to achieve a sparse solution, thus improving the ability of the selected features to discriminate between labels. Extensive experiments on both synthetic and real-world PML datasets validate that PMLFS-MEN achieves superior performance over state-of-the-art partial and traditional multi-label feature selection methods.