Feature　selection　is　a　commonly　utilized　methodology　in　multi-label　learning　(MLL)　for　tackling　the　challenge　of　high-dimensional　data.　Accurate　annotation　of　relevant　labels　is　crucial　for　successful　multi-label　feature　selection　(MFS).　Nevertheless,　multi-label　datasets　frequently　consist　of　ground-truth　and　noisy　labels　in　real-world　applications,　giving　rise　to　the　partial　multi-label　learning　(PML)　problem.　The　inclusion　of　noisy　labels　complicates　the　task　of　conventional　MFS　methods　in　accurately　identifying　the　optimal　features　subset　in　such　datasets.　To　tackle　this　issue,　we　propose　a　novel　partial　multi-label　feature　selection　method　with　low-rank　sparse　factorization　and　manifold　learning,　called　PMFS-LRS.　Specifically,　we　first　decompose　the　candidate　label　matrix　into　two　distinct　components:　a　low-rank　matrix　referring　to　ground-truth　labels　and　a　sparse　matrix　referring　to　noisy　labels.　This　decomposition　allows　PMFS-LRS　to　effectively　distinguish　noise　labels　from　ground-truth　labels,　thereby　mitigating　the　impact　of　noisy　data.　Then,　the　local　label　correlations　are　explored　using　a　manifold　learning　framework　to　improve　the　label　disambiguation　performance.　Finally,　a　l(2,1)-norm　regularization　is　integrated　into　the　objective　function　to　facilitate　effective　feature　selection.　Comprehensive　experiments　conducted　on　both　real-world　and　synthetic　PML　datasets　demonstrate　that　PMFS-LRS　is　superior　to　several　existing　state-of-the-art　MFS　methods.