Traditional　filter-based　feature　selection　methods　calculate　some　scores　of　each　feature　independently　to　select　features　in　a　statistical　or　geometric　perspective　only,　however,　they　ignore　the　correlation　of　different　features.　To　solve　this　problem,　an　unsupervised　feature　selection　method　based　on　locality　preserving　projection　and　sparse　representation　is　proposed.　The　nonnegativity　and　sparsity　of　feature　weights　are　limited　to　select　features　in　the　proposed　method.　The　experimental　results　on　4　gene　expression　datasets　and　2　image　datasets　show　that　the　method　is　effective.　©,　2015,　Journal　of　Pattern　Recognition　and　Artificial　Intelligence.　All　right　reserved.