The　brightness　temperature　(T-B)　features　extracted　from　Chang＇e　Lunar　Microwave　Sounder　(CELMS)　data　have　been　proved　their　superiority　to　study　mare　basalt.　In　this　paper,　dimension　reduction　and　feature　space　analysis　are　conducted　on　T-B　features　to　fully　understand　the　data　distribution　and　reduce　the　feature　redundancy　in　the　classification　process　based　on　two　methods　-　Principal　Component　Analysis　(PCA)　and　Nonnegative　Matrix　Factorization　(NNMF).　The　results　showed　that　PCA　and　NNMF　can　effectively　enhance　the　classification　capability　of　early(?)-　and　late(?)-age　mare　basalt　respectively,　and　proved　the　necessity　of　dimension　reduction　for　CELMS　T-B　features　due　to　the　largely-existing　redundancy.