The　traditional　clustering　methods　are　inefficient　due　to　high　dimension　and　redundancy,　small　sample　size　and　noise　of　the　gene　expression　data.　Subspace　segmentation　is　an　effective　method　for　high　dimensional　data　clustering.　However,　the　performance　of　clustering　is　reduced　by　using　subspace　segmentation　on　the　gene　expression　data　directly.　To　cluster　the　gene　expression　data　more　effectively,　low　rank　projection　least　square　regression　subspace　segmentation　method(LPLSR)　is　proposed.　The　improved　low　rank　method　is　utilized　to　project　gene　expression　data　into　the　latent　subspace　to　remove　the　possible　corruptions　in　data　and　get　a　relatively　clean　data　dictionary.　Then,　least　square　regression　method　is　employed　to　obtain　the　low-dimension　representation　for　data　vectors　and　the　affinity　matrix　is　constructed　to　cluster　the　gene　data.　The　experimental　results　on　six　public　gene　expression　datasets　show　the　validity　of　the　proposed　method.　©　2017,　Science　Press.　All　right　reserved.