IEEE Trans Pattern Anal Mach Intell - Constrained Nonnegative Matrix Factorization for Image Representation.

Tópicos

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Resumo

Nonnegative matrix factorization (NMF) is a popular technique for finding parts-based, linear representations of nonnegative data. It has been successfully applied in a wide range of applications such as pattern recognition, information retrieval, and computer vision. However, NMF is essentially an unsupervised method and cannot make use of label information. In this paper, we propose a novel semi-supervised matrix decomposition method, called Constrained Nonnegative Matrix Factorization (CNMF), which incorporates the label information as additional constraints. Specifically, we show how explicitly combining label information improves the discriminating power of the resulting matrix decomposition. We explore the proposed CNMF method with two cost function formulations and provide the corresponding update solutions for the optimization problems. Empirical experiments demonstrate the effectiveness of our novel algorithm in comparison to the state-of-the-art approaches through a set of evaluations based on real-world applications.

Resumo Limpo

nonneg matrix factor nmf popular techniqu find partsbas linear represent nonneg data success appli wide rang applic pattern recognit inform retriev comput vision howev nmf essenti unsupervis method make use label inform paper propos novel semisupervis matrix decomposit method call constrain nonneg matrix factor cnmf incorpor label inform addit constraint specif show explicit combin label inform improv discrimin power result matrix decomposit explor propos cnmf method two cost function formul provid correspond updat solut optim problem empir experi demonstr effect novel algorithm comparison stateoftheart approach set evalu base realworld applic

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