GONÇALVES , Paulo

International audience

International audience

International audience

International audience

Graph Semi-supervised learning (gSSL) aims to classify data exploiting two initial inputs: firstly, the data are structured in a network whose edges convey information on the proximity, in a wide sense, of two data points (e.g. correlation or spatial proximity) and, second, there is a partial information on some nodes, which have previously...

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...

Classification through Graph-based semi-supervised learning algorithms can be viewed as a diffusion process with restart on the labels. In this work, we exploit this equivalence to introduce a novel algorithm which relies on the formulation of a non-local diffusion process, fueled by the γ-th power of the standard Laplacian matrix Lγ, with 0 <...

We study the problem of parametric modeling of network-structured signals with graph filters. To benefit from the properties of several graph shift operators simultaneously, and to enhance interpretability, we investigate combinations of parallel graph filters with different shift operators. Due to their extra degrees of freedom, these models...