International audienceIn this paper, we introduce a new family of graph-based operators for semi-supervised and unsupervised classification. These operators interpolate between two morphological gradient operators introduced on graphs, and are linked with the discrete infinity Laplacian. Then, we consider semi-supervised classification as the Dirichlet problem associated with this new family of operators. We show the proof of existence and uniqueness of the solution of this problem and propose an implementation. Similarly, we consider unsupervised classification as a diffusion problem associated with this new family of operators to handle it. We finally illustrate these two approaches on image segmentation and data clustering
A foundational problem in semi-supervised learning is the construction of a graph underlying the dat...
Recent years have seen a growing number of graph-based semi-supervised learning methods. While the l...
Discussions about different graph Laplacian, mainly normalized and unnormalized versions of graph La...
International audienceIn this paper, we introduce a new family of graph-based operators for semi-sup...
International audienceIn this paper, we introduce a new class of nonlocal p-Laplacian operators that...
In recent years, the need for pattern recognition and data analysis has grown exponentially in vario...
National audienceClassification through Graph-based semi-supervised learning algorithms can be viewe...
International audienceIn this paper, an adaptation of the infinity laplacian equation to the case of...
International audienceIn this paper, an adaptation of the infinity Laplacian equation to weighted gr...
Abstract. In this paper we present a computationally efficient algorithm utilizing a fully or semino...
We consider the task of classifying when an extremely reduced amount of labelled data is available. ...
International audienceIn this paper, we consider the adaptation of two Partial Differential Equation...
Partial differential equations (PDEs) play a key role in the mathematicalmodelization of phenomena i...
Semi-supervised clustering algorithms aim to improve clustering results using limited supervision. T...
A foundational problem in semi-supervised learning is the construction of a graph underlying the dat...
Recent years have seen a growing number of graph-based semi-supervised learning methods. While the l...
Discussions about different graph Laplacian, mainly normalized and unnormalized versions of graph La...
International audienceIn this paper, we introduce a new family of graph-based operators for semi-sup...
International audienceIn this paper, we introduce a new class of nonlocal p-Laplacian operators that...
In recent years, the need for pattern recognition and data analysis has grown exponentially in vario...
National audienceClassification through Graph-based semi-supervised learning algorithms can be viewe...
International audienceIn this paper, an adaptation of the infinity laplacian equation to the case of...
International audienceIn this paper, an adaptation of the infinity Laplacian equation to weighted gr...
Abstract. In this paper we present a computationally efficient algorithm utilizing a fully or semino...
We consider the task of classifying when an extremely reduced amount of labelled data is available. ...
International audienceIn this paper, we consider the adaptation of two Partial Differential Equation...
Partial differential equations (PDEs) play a key role in the mathematicalmodelization of phenomena i...
Semi-supervised clustering algorithms aim to improve clustering results using limited supervision. T...
A foundational problem in semi-supervised learning is the construction of a graph underlying the dat...
Recent years have seen a growing number of graph-based semi-supervised learning methods. While the l...
Discussions about different graph Laplacian, mainly normalized and unnormalized versions of graph La...