9 citations · 9 across the 4 of their papers we have counts for
5 papers · 1 filter
Spectral Analysis Of Weighted Laplacians Arising In Data Clustering
Franca Hoffmann, Bamdad Hosseini, Assad A. Oberai +1
Graph Laplacians computed from weighted adjacency matrices are widely used to identify geometric structure in data, and clusters in particular; their spectral properties play a cen…
Consistency of semi-supervised learning algorithms on graphs: Probit and one-hot methods
Franca Hoffmann, Bamdad Hosseini, Zhi Ren +1
Graph-based semi-supervised learning is the problem of propagating labels from a small number of labelled data points to a larger set of unlabelled data. This paper is concerned wi…
Uniqueness of stationary states for singular Keller-Segel type models
Vincent Calvez, Jose Antonio Carrillo, Franca Hoffmann
We consider a generalised Keller-Segel model with non-linear porous medium type diffusion and non-local attractive power law interaction, focusing on potentials that are more singu…
Interacting Langevin Diffusions: Gradient Structure And Ensemble Kalman Sampler
Alfredo Garbuno-Inigo, Franca Hoffmann, Wuchen Li +1
Solving inverse problems without the use of derivatives or adjoints of the forward model is highly desirable in many applications arising in science and engineering. In this paper,…
Geometric structure of graph Laplacian embeddings
Nicolas Garcia Trillos, Franca Hoffmann, Bamdad Hosseini
We analyze the spectral clustering procedure for identifying coarse structure in a data set , and in particular study the geometry of graph Laplacian embeddings wh…