Non-negative sparse coding

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dc.creatorHoyer, Patrik O.
dc.date2002-02-11
dc.date.accessioned2026-07-07T03:18:06Z
dc.date.available2026-07-07T03:18:06Z
dc.descriptionNon-negative sparse coding is a method for decomposing multivariate data into non-negative sparse components. In this paper we briefly describe the motivation behind this type of data representation and its relation to standard sparse coding and non-negative matrix factorization. We then give a simple yet efficient multiplicative algorithm for finding the optimal values of the hidden components. In addition, we show how the basis vectors can be learned from the observed data. Simulations demonstrate the effectiveness of the proposed method.
dc.identifierhttps://arxiv.org/abs/cs/0202009
dc.identifierhttp://arxiv.org/abs/cs/0202009
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/30982
dc.subjectNeural and Evolutionary Computing
dc.subjectComputer Vision and Pattern Recognition
dc.subjectI.4.2
dc.titleNon-negative sparse coding
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