Semi-supervised　nonnegative　matrix　factorization　(NMF)　has　received　considerable　attention　in　machine　learning　and　data　mining.　A　new　semi-supervised　NMF　method,　called　dual　semi-supervised　convex　nonnegative　matrix　factorization　(DCNMF),　is　proposed　in　this　paper　for　fully　using　the　limited　label　information.　Specifically,　DCNMF　simultaneously　incorporates　the　pointwise　and　pairwise　constraints　of　labeled　samples　as　dual　supervisory　information　into　convex　NMF,　which　results　in　a　better　low-dimensional　data　representation.　Moreover,　DCNMF　imposes　the　nonnegative　constraint　only　on　the　coefficient　matrix　but　not　on　the　base　matrix.　Consequently,　DCNMF　can　process　mixed-sign　data,　and　hence　enlarge　the　range　of　applications.　We　derive　an　efficient　alternating　iterative　algorithm　for　DCNMF　to　solve　the　optimization,　and　analyze　the　proposed　DCNMF　method　in　terms　of　the　convergence　and　computational　complexity.　We　also　discuss　the　relationships　between　DCNMF　and　several　typical　NMF　based　methods.　Experimental　results　illustrate　that　DCNMF　outperforms　the　related　state-of-the-art　NMF　methods　on　nonnegative　and　mixed-sign　datasets　for　clustering　applications.(c)　2021　Elsevier　Inc.　All　rights　reserved.