Human　poses　admit　complicated　articulations　and　multigranular　similarity.　Previous　works　on　learning　human　pose　metric　utilize　sparse　models,　which　concentrate　large　weights　on　highly　similar　poses　and　fail　to　depict　an　overall　structure　of　poses　with　multigranular　similarity.　Moreover,　previous　works　require　a　large　number　of　similar/dissimilar　annotated　pairwise　poses,　which　is　an　tedious　task　and　remains　inaccurate　due　to　different　subjective　judgments　of　experts.　Motivated　by　graph-based　neighbor　assignment　techniques,　we　propose　an　unsupervised　model　called　sparsity　locality　preserving　projection　with　adaptive　neighbors　(SLPPAN),　for　learning　human　pose　distance　metric.　By　using　a　property　of　the　graph　Laplacian,　SLPPAN　introduces　a　fixed-rank　constraint　to　enforce　an　adaptive　graph　structure　of　poses　and　learns　the　neighbor　assignment,　the　similarity　measurement,　and　pose　metric　simultaneously.　Experiments　on　pose　retrieval　of　the　CMU　Mocap　database　demonstrate　that　SLPPAN　outperforms　traditional　pose　metric　learning　methods　by　capturing　viewpoint　variations　of　human　poses.　Experiments　on　keyframe　extraction　of　the　MSRAction3D　database　demonstrate　that　SLPPAN　outperforms　current　methods　by　precisely　detecting　important　frames　of　action　sequences.