Multi-view　unsupervised　feature　selection　(MUFS)　has　received　considerable　attention　in　recent　years.　Existing　MUFS　methods　for　processing　unlabeled　incomplete　multi-view　data,　where　some　samples　are　missing　in　certain　views,　first　impute　the　missing　values　and　then　perform　feature　selection　on　the　completed　dataset.　However,　treating　imputation　and　feature　selection　as　two　separate　processes　overlooks　their　potential　interactions.　The　graph-guided　local　structure　gleaned　from　feature　selection　can　aid　in　imputation,　which　in　turn　can　enhance　the　feature　selection　performance.　Additionally,　most　similarity　graph-based　MUFS　methods　suffer　from　high　computational　costs.　To　address　these　problems,　we　propose　a　novel　MUFS　method,　termed　Tensorial　Incomplete　Multi-view　unsupErvised　Feature　Selection　(TIME-FS).　TIME-FS　unifies　missing　value　recovery,　discriminative　feature　selection,　and　low-dimensional　representation　learning　within　a　joint　framework　through　matrix　decomposition.　Then,　TIME-FS　conducts　CP　decomposition　on　tensor　data　formed　by　the　low-dimensional　representations　of　different　views　to　learn　a　consistent　anchor　graph　across　views　and　a　view-preference　weight　matrix,　both　of　which　simultaneously　guide　missing　view　imputation　and　feature　selection.　Furthermore,　an　efficient　algorithm　with　low　time　complexity　and　rapid　convergence　is　proposed　to　solve　TIME-FS.　Extensive　experimental　results　demonstrate　the　effectiveness　and　efficiency　of　TIME-FS　over　state-of-the-art　methods.