Feature　selection　is　an　important　data　preprocessing　in　data　mining　and　machine　learning,　that　can　reduce　the　number　of　features　without　deteriorating　model＇s　performance.　Recently,　sparse　regression　has　received　considerable　attention　in　feature　selection　task　due　to　its　good　performance.　However,　because　the　l(2,0)-norm　regularization　term　is　non-convex,　this　problem　is　hard　to　solve,　and　most　of　the　existing　methods　relaxed　it　by　l(2,1)-norm.　Unlike　the　existing　methods,　this　paper　proposes　a　novel　method　to　solve　the　l(2,0)-norm　regularized　least　squares　problem　directly　based　on　iterative　hard　thresholding,　which　can　produce　exact　row-sparsity　solution　for　weights　matrix,　and　features　can　be　selected　more　precisely.　Furthermore,　two　homotopy　strategies　are　derived　to　reduce　the　computational　time　of　the　optimization　method,　which　are　more　practical　for　real-world　applications.　The　proposed　method　is　verified　on　eight　biological　datasets,　experimental　results　show　that　our　method　can　achieve　higher　classification　accuracy　with　fewer　number　of　selected　features　than　the　approximate　convex　counterparts　and　other　state-of-the-art　feature　selection　methods.