This　study　proposes　a　novel　human　action　recognition　method　using　regularized　multi-task　learning.　First,　we　propose　the　part　Bag-of-Words　(PBoW)　representation　that　completely　represents　the　local　visual　characteristics　of　the　human　body　structure.　Each　part　can　be　viewed　as　a　single　task　in　a　multi-task　learning　formulation.　Further,　we　formulate　the　task　of　multi-view　human　action　recognition　as　a　learning　problem　penalized　by　a　graph　structure　that　is　built　according　to　the　human　body　structure.　Our　experiments　show　that　this　method　has　significantly　better　performance　in　human　action　recognition　than　the　standard　Bag-of-Words　+　Support　Vector　Machine　(BOW　+　SVM)　method　and　other　state-of-the-art　methods.　Further,　the　performance　of　the　proposed　method　with　simple　global　representation　is　as　good　as　that　of　state-of-the-art　methods　for　human　action　recognition　on　the　TJU　dataset　(a　new　multi-view　action　dataset　with　RGB,　depth,　and　skeleton　data,　which　has　been　created　by　our　group).　(C)　2015　Elsevier　Inc.　All　rights　reserved.