This　paper　presents　a　pruned　sparse　extreme　learning　machine　(PS-ELM)　algorithm,　which　can　generate　a　compact　single-hidden-layer　neural　network　(SLNN)　by　automatically　pruning　the　number　of　hidden　nodes　while　keep　high　accuracy.　In　this　PS-ELM　algorithm,　input　connections　between　input　and　hidden　layers　are　base　vectors,　which　can　sparsely　map　the　input　features　into　hidden　layer　by　using　gradient　projection　(GP)　algorithm;　The　output　weights　between　hidden　and　output　layers　can　map　the　sparse　features　into　class　labels.　This　PS-ELM　algorithm　initializes　the　SLNN　given　superfluous　number　of　hidden　nodes.　The　subsequent　training　process　consists　of　four　iterative　steps.　The　first　one　is　to　update　base　vectors　by　using　Lagrange　dual　optimization.　The　second　one　is　to　prune　zero　base　vectors　which　are　considered　to　be　insignificant.　The　third　one　is　sparse　coding　which　re-encodes　the　training　samples　given　remained　base　vectors.　The　fourth　one　is　to　update　output　weight　matrix　by　using　ELM-like　algorithm.　This　iterative　process　stops　once　the　number　of　hidden　nodes　at　current　time　is　equal　to　the　number　at　the　last　time.　This　PS-ELM　algorithm　can　improve　the　sparsity　and　distinction　of　hidden　layer　feature　representations.　Meanwhile,　the　pruning　is　independent　on　hand-designed　threshold.　Experimental　results　on　benchmark　datasets　have　shown　that　the　PS-ELM　algorithm　can　automatically　achieve　a　reasonable　compact　network　structure　while　keep　comparable　or　much　higher　accuracy　in　classification.　©　2016　IEEE.