Subspace　segmentation　is　an　efficient　tool　in　high　dimensional　data　clustering.　However,　the　construction　of　affine　matrix　and　the　clustering　result　are　directly　affected　by　missing　data　and　noise　data.　To　solve　this　problem,　latent　least　square　regression　for　subspace　segmentation　(LatLSR)　is　proposed.　The　data　matrix　is　reconstructed　in　directions　of　column　and　row,　respectively.　Two　re-constructed　coefficient　matrices　are　optimized　alternately,　and　thus　the　information　in　two　directions　is　fully　considered.　The　experimental　results　on　six　gene　expression　datasets　show　that　the　proposed　method　produces　better　performance　than　the　existing　subspace　segmentation　methods.　©　2016,　Science　Press.　All　right　reserved.