Deep　learning　has　become　one　of　very　important　machine　learning　methods　in　image　classification,　but　most　of　them　require　a　long　training　time　to　solve　a　non-convex　optimization　problem.　In　comparison,　the　training　of　extreme　learning　machine　(ELM)　is　very　simple,　fast　and　effective.　In　order　to　combine　the　advantages　of　both　methods,　many　researchers　have　tried　to　introduce　ELM　to　deep　architectures　(Kasun　et　al.　in　IEEE　Intell　Syst　28:31-34,　2013;　Yu　et　al.　in　Neurocomputing　149:308-315,　2015;　Tissera　and　McDonnell　in　Neurocomputing　174:42-49,　2016　and　in:　Proceedings　of　ELM-2014,　vol　1,　Proceedings　in　adaptation,　learning　and　optimization,　vol　3,　2016;　Junying　et　al.　in　Neurocomputing　171:63-72,　2016;　Uzair　et　al.　in　Neural　Comput　Appl,　2015)　to　solve　unsupervised　learning　and　supervised　learning　problems.　In　this　paper,　we　propose　a　new　deep　neural　network　based　on　ELM　called　discriminative　deep　ELM　(DDELM)　to　address　the　semi-supervised　learning　problems　in　image　classification.　The　proposed　deep　architecture　consists　of　several　stacked　unsupervised　ELMs　and　an　additional　label　layer　on　the　top　layer　of　the　stacked　model.　Experiments　on　three　standard　image　data　show　that　DDELM　outperforms　both　representative　semi-supervised　learning　algorithms　and　existing　deep　architectures　such　as　DCNN　in　terms　of　accuracy　and　training　time.