A class of Laplacian multiwavelets bases for high-dimensional data

By Nir Sharon, Yoel Shkolnisky. 2015

12 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Numerical Methods & Scientific Computing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Michael M. Bronstein
2. Emanuele Rodolà
3. Davide Boscaini
4. Jonathan Masci
5. Federico Monti

In Computer Vision and Pattern Recognition

1. Michael M. Bronstein
2. Federico Monti
3. Emanuele Rodolà
4. Davide Boscaini
5. Jonathan Masci
6. A. Bronstein
7. Luc Van Gool
8. Jan Svoboda

Citations in the corpus

• Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs (2016, methodology): “[47]. Spectral analysis techniques were extended to graphs considering the orthogonal eigenfunctions of the Laplacian operator as a generalization of the Fourier basis. Constructions such as wavelets [14, 18, 21, 39, 45] or algorithms such as dictionary learning [62], Lasso [9], PCA [43, 44], or mat”