Multiview　subspace　clustering　(MSC)　maximizes　the　utilization　of　complementary　description　information　provided　by　multiview　data　and　achieves　impressive　clustering　performance.　However,　most　of　them　are　inefficient　or　even　invalid　among　large-scale　scenarios　due　to　expensive　computational　complexity.　Recently,　anchor　strategy　has　been　developed　to　address　this,　which　selects　a　few　representative　samples　as　anchor　points　for　representation　learning　and　anchor　graph　construction.　However,　most　of　them　only　explore　single　cross-view　correlation,　i.e.,　cross-view　consistency　from　the　global　aspect　or　cross-view　complementarity　from　the　local　aspect,　which　provides　insufficient　semantic　correlation　understanding　and　exploration　for　complex　multiview　data.　To　effectively　address　this　issue,　this　study　proposes　a　fast　multiview　subspace　clustering　(FMSC)　with　local-global　anchor　representation　collaborative　learning.　FMSC　integrates　the　discriminative　anchor　points　learning　and　anchor　graph　construction　with　optimal　structure　into　a　joint　framework.　Furthermore,　local　(view-specific)　and　global　(view-shared)　anchor　representations　are　learned　collaboratively　under　two　interaction　strategies　at　different　levels,　providing　beneficial　guidance　from　global　learning　to　local　learning.　Thus,　the　proposed　FMSC　can　maximize　the　exploration　of　the　complementarity-consistency　among　multiview　data　and　capture　a　more　comprehensive　semantic　correlation.　More　importantly,　an　effective　algorithm　with　linear　complexity　is　designed　to　solve　the　corresponding　optimization　problem　of　FMSC,　making　it　more　practical　in　large-scale　clustering　tasks.　Extensive　experimental　results　confirm　the　superiority　of　the　proposed　FMSC　in　both　clustering　performance　and　computational　efficiency.