Finding latent structures in data is drawing increasing attention in diverse fields such as
image and signal processing, fluid dynamics, and machine learning. In this work we …

We analyse the functional 𝒥 (u)=∥∇ u∥∞ defined on Lipschitz functions with
homogeneous Dirichlet boundary conditions. Our analysis is performed directly on the …

The p-Laplacian is a non-linear generalization of the Laplace operator. In the graph context,
its eigenfunctions are used for data clustering, spectral graph theory, dimensionality …

Extracting the latent underlying structures of complex nonlinear local and nonlocal flows is
essential for their analysis and modeling. In this Element the authors attempt to provide a …

Evolutionary weighted Laplace equations with convex constant coefficients and variable
coefficients consisting of power functions are employed to improve signal decomposition …

Our aim is to explain and characterize the behavior of adaptive total variation (TV)
regularization. TV has been widely used as an edge-preserving regularizer. However …

We present a comprehensive analysis of total variation (TV) on non-Euclidean domains and
its eigenfunctions. We specifically address parameterized surfaces, a natural representation …

Extracting the latent underlying structures of complex nonlinear local and nonlocal flows is
essential for their analysis and modeling. In this work, we attempt to provide a consistent …

Non-linear spectral decompositions of images based on one-homogeneous functionals
such as total variation have gained considerable attention in the last few years. Due to their …

Neural networks have revolutionized the field of data science, yielding remarkable solutions
in a data-driven manner. For instance, in the field of mathematical imaging, they have …