Data　representation　learning　is　one　of　the　most　important　problems　in　machine　learning.　Unsupervised　representation　learning　becomes　meritorious　as　it　has　no　necessity　of　label　information　with　observed　data.　Due　to　the　highly　time-consuming　learning　of　deep-learning　models,　there　are　many　machine-learning　models　directly　adapting　well-trained　deep　models　that　are　obtained　in　a　supervised　and　end-to-end　manner　as　feature　abstractors　to　distinct　problems.　However,　it　is　obvious　that　different　machine-learning　tasks　require　disparate　representation　of　original　input　data.　Taking　human　action　recognition　as　an　example,　it　is　well　known　that　human　actions　in　a　video　sequence　are　3-D　signals　containing　both　visual　appearance　and　motion　dynamics　of　humans　and　objects.　Therefore,　the　data　representation　approaches　with　the　capabilities　to　capture　both　spatial　and　temporal　correlations　in　videos　are　meaningful.　Most　of　the　existing　human　motion　recognition　models　build　classifiers　based　on　deep-learning　structures　such　as　deep　convolutional　networks.　These　models　require　a　large　quantity　of　training　videos　with　annotations.　Meanwhile,　these　supervised　models　cannot　recognize　samples　from　the　distinct　dataset　without　retraining.　In　this　article,　we　propose　a　new　3-D　deconvolutional　network　(3DDN)　for　representation　learning　of　high-dimensional　video　data,　in　which　the　high-level　features　are　obtained　through　the　optimization　approach.　The　proposed　3DDN　decomposes　the　video　frames　into　spatiotemporal　features　under　a　sparse　constraint　in　an　unsupervised　way.　In　addition,　it　also　can　be　regarded　as　a　building　block　to　develop　deep　architectures　by　stacking.　The　high-level　representation　of　input　sequential　data　can　be　used　in　multiple　downstream　machine-learning　tasks,　we　evaluate　the　proposed　3DDN　and　its　deep　models　in　human　action　recognition.　The　experimental　results　from　three　datasets:　1)　KTH　data;　2)　HMDB-51;　and　3)　UCF-101,　demonstrate　that　the　proposed　3DDN　is　an　alternative　approach　to　feedforward　convolutional　neural　networks　(CNNs),　that　attains　comparable　results.　©　2013　IEEE.