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Fractional Graph-based Semi-Supervised Learning

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Abstract

Graph-based semi-supervised learning for classifica- tion endorses a nice interpretation in terms of diffusive random walks, where the regularisation factor in the original optimisation formulation plays the role of a restarting probability. Recently, a new type of biased random walks for characterising certain dynamics on networks have been defined and rely on the γ- th power of the standard Laplacian matrix Lγ, with γ > 0. In particular, these processes embed long range transitions, the Le ́vy flights, that are capable of one-step jumps between far- distant states (nodes) of the graph. The present contribution envisions to build upon these volatile random walks to propose a new version of graph based semi-supervised learning algorithms whose classification outcome could benefit from the dynamics induced by the fractional transition matrix
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Dates and versions

hal-01586767 , version 1 (13-09-2017)

Identifiers

  • HAL Id : hal-01586767 , version 1

Cite

Sarah de Nigris, Esteban Bautista, Patrice Abry, Konstantin Avrachenkov, Paulo Gonçalves. Fractional Graph-based Semi-Supervised Learning. EUSIPCO 2017 - 25th European Signal Processing Conference, Aug 2017, Kos Island, Greece. ⟨hal-01586767⟩
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