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We consider the problem of learning an unknown (overcomplete) basis from an unknown sparse linear combination. Introducing the "sparse coding neural gas" algorithm, we show how to employ a combination of the original neural gas algorithm and Oja's rule in order to learn a simple sparse code that represents each training sample by a multiple of one basis vector. We generalise this algorithm using orthogonal matching pursuit in order to learn a sparse code where each training sample isdblp:conf/esann/LabuschBM08 fatcat:pkdw6pj6hfbvnmrnl4bzbpwjei