Unsupervised　feature　selection　is　an　efficient　approach　of　dimensionality　reduction　for　alleviating　the　curse　of　dimensionality　in　the　countless　unlabeled　high-dimensional　data.　In　view　of　the　sparseness　of　the　high-dimensional　data,　we　propose　a　robust　neighborhood　embedding　(RNE)　method　for　unsupervised　feature　selection.　First,　with　the　fact　that　each　data　point　and　its　neighbors　are　close　to　a　locally　linear　patch　of　some　underlying　manifold,　we　obtain　the　feature　weight　matrix　through　the　locally　linear　embedding　(LLE)　algorithm.　Second,　we　use　ℓ1-norm　to　describe　reconstruction　error　minimization,　i.e.,　loss　function　to　suppress　the　impact　of　outlier　and　noises　in　the　dataset.　As　the　RNE　model　is　convex　but　non-smooth,　we　exploit　alternation　direction　method　of　multipliers　(ADMM)　to　solve　it.　Finally,　extensive　experimental　results　on　benchmark　datasets　validate　that　the　RNE　method　is　effective　and　superior　to　the　state-of-the-art　unsupervised　feature　selection　algorithms　in　terms　of　clustering　performance.　©　2020　Elsevier　B.V.