Multi-view　human　action　recognition　remains　a　challenging　problem　due　to　large　view　changes.　In　this　article,　we　propose　a　transfer　learning-based　framework　called　transferable　dictionary　learning　and　view　adaptation　(TDVA)　model　for　multi-view　human　action　recognition.　In　the　transferable　dictionary　learning　phase,　TDVA　learns　a　set　of　view-specific　transferable　dictionaries　enabling　the　same　actions　from　different　views　to　share　the　same　sparse　representations,　which　can　transfer　features　of　actions　from　different　views　to　an　intermediate　domain.　In　the　view　adaptation　phase,　TDVA　comprehensively　analyzes　global,　local,　and　individual　characteristics　of　samples,　and　jointly　learns　balanced　distribution　adaptation,　locality　preservation,　and　discrimination　preservation,　aiming　at　transferring　sparse　features　of　actions　of　different　views　from　the　intermediate　domain　to　a　common　domain.　In　other　words,　TDVA　progressively　bridges　the　distribution　gap　among　actions　from　various　views　by　these　two　phases.　Experimental　results　on　IXMAS,　ACT4(2),　and　NUCLA　action　datasets　demonstrate　that　TDVA　outperforms　state-of-the-art　methods.