As　one　of　supervised　learning　algorithms,　extreme　learning　machine　(ELM)　has　been　proposed　for　training　single-hidden-layer　feedforward　neural　networks　and　shown　great　generalization　performance.　ELM　randomly　assigns　the　weights　and　biases　between　input　and　hidden　layers　and　only　learns　the　weights　between　hidden　and　output　layers.　Physiological　research　has　shown　that　neurons　at　the　same　layer　are　laterally　inhibited　to　each　other　such　that　outputs　of　each　layer　are　sparse.　However,　it　is　difficult　for　ELM　to　accommodate　the　lateral　inhibition　by　directly　using　random　feature　mapping.　Therefore,　this　paper　proposes　a　sparse　coding　ELM　(ScELM)　algorithm,　which　can　map　the　input　feature　vector　into　a　sparse　representation.　In　this　proposed　ScELM　algorithm,　an　unsupervised　way　is　used　for　sparse　coding　and　dictionary　is　randomly　assigned　rather　than　learned.　Gradient　projection　based　method　is　used　for　the　sparse　coding.　The　output　weights　are　trained　through　the　same　supervised　way　as　ELM.　Experimental　results　on　the　benchmark　datasets　have　shown　that　this　proposed　ScELM　algorithm　can　outperform　other　state-of-the-art　methods　in　terms　of　classification　accuracy.　©　2017　Elsevier　B.V.