The　recent　development　of　microarray　gene　expression　techniques　have　made　it　possible　to　offer　phenotype　classification　of　many　diseases.　However,　in　gene　expression　data　analysis,　each　sample　is　represented　by　quite　a　large　number　of　genes,　and　many　of　them　are　redundant　or　insignificant　to　clarify　the　disease　problem.　Therefore,　how　to　efficiently　select　the　most　useful　genes　has　been　becoming　one　of　the　most　hot　research　topics　in　the　gene　expression　data　analysis.　In　this　paper,　a　novel　unsupervised　twostage　coarse-fine　gene　selection　method　is　proposed.　In　the　first　stage,　we　apply　the　kmeans　algorithm　to　over-cluster　the　genes　and　discard　some　redundant　genes.　In　the　second　stage,　we　select　the　most　representative　genes　from　the　remaining　ones　based　on　matrix　factorization.　Finally　the　experimental　results　on　several　data　sets　are　presented　to　show　the　effectiveness　of　our　method.