IEEE Trans Pattern Anal Mach Intell - Graph Regularized Nonnegative Matrix Factorization for Data Representation.

Tópicos

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Resumo

Matrix factorization techniques have been frequently applied in information retrieval, computer vision, and pattern recognition. Among them, Nonnegative Matrix Factorization (NMF) has received considerable attention due to its psychological and physiological interpretation of naturally occurring data whose representation may be parts based in the human brain. On the other hand, from the geometric perspective, the data is usually sampled from a low-dimensional manifold embedded in a high-dimensional ambient space. One then hopes to find a compact representation,which uncovers the hidden semantics and simultaneously respects the intrinsic geometric structure. In this paper, we propose a novel algorithm, called Graph Regularized Nonnegative Matrix Factorization (GNMF), for this purpose. In GNMF, an affinity graph is constructed to encode the geometrical information and we seek a matrix factorization, which respects the graph structure. Our empirical study shows encouraging results of the proposed algorithm in comparison to the state-of-the-art algorithms on real-world problems.

Resumo Limpo

matrix factor techniqu frequent appli inform retriev comput vision pattern recognit among nonneg matrix factor nmf receiv consider attent due psycholog physiolog interpret natur occur data whose represent may part base human brain hand geometr perspect data usual sampl lowdimension manifold embed highdimension ambient space one hope find compact representationwhich uncov hidden semant simultan respect intrins geometr structur paper propos novel algorithm call graph regular nonneg matrix factor gnmf purpos gnmf affin graph construct encod geometr inform seek matrix factor respect graph structur empir studi show encourag result propos algorithm comparison stateoftheart algorithm realworld problem

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