IEEE Trans Neural Netw Learn Syst - Fick's Law Assisted Propagation for Semisupervised Learning.

Tópicos

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Resumo

How to propagate the label information from labeled examples to unlabeled examples is a critical problem for graph-based semisupervised learning. Many label propagation algorithms have been developed in recent years and have obtained promising performance on various applications. However, the eigenvalues of iteration matrices in these algorithms are usually distributed irregularly, which slow down the convergence rate and impair the learning performance. This paper proposes a novel label propagation method called Fick's law assisted propagation (FLAP). Unlike the existing algorithms that are directly derived from statistical learning, FLAP is deduced on the basis of the theory of Fick's First Law of Diffusion, which is widely known as the fundamental theory in fluid-spreading. We prove that FLAP will converge with linear rate and show that FLAP makes eigenvalues of the iteration matrix distributed regularly. Comprehensive experimental evaluations on synthetic and practical datasets reveal that FLAP obtains encouraging results in terms of both accuracy and efficiency.

Resumo Limpo

propag label inform label exampl unlabel exampl critic problem graphbas semisupervis learn mani label propag algorithm develop recent year obtain promis perform various applic howev eigenvalu iter matric algorithm usual distribut irregular slow converg rate impair learn perform paper propos novel label propag method call fick law assist propag flap unlik exist algorithm direct deriv statist learn flap deduc basi theori fick first law diffus wide known fundament theori fluidspread prove flap will converg linear rate show flap make eigenvalu iter matrix distribut regular comprehens experiment evalu synthet practic dataset reveal flap obtain encourag result term accuraci effici

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