-
Geometric Wavelet Scattering Networks on Compact Riemannian Manifolds
The Euclidean scattering transform was introduced nearly a decade ago to...
read it
-
Geometric Scattering on Manifolds
We present a mathematical model for geometric deep learning based upon a...
read it
-
Understanding Graph Neural Networks with Asymmetric Geometric Scattering Transforms
The scattering transform is a multilayered wavelet-based deep learning a...
read it
-
Geometric Scattering Attention Networks
Geometric scattering has recently gained recognition in graph representa...
read it
-
Efficient and Stable Graph Scattering Transforms via Pruning
Graph convolutional networks (GCNs) have well-documented performance in ...
read it
-
Unsupervised Deep Haar Scattering on Graphs
The classification of high-dimensional data defined on graphs is particu...
read it
-
Scattering Transform Based Image Clustering using Projection onto Orthogonal Complement
In the last few years, large improvements in image clustering have been ...
read it
Graph Classification with Geometric Scattering
One of the most notable contributions of deep learning is the application of convolutional neural networks (ConvNets) to structured signal classification, and in particular image classification. Beyond their impressive performances in supervised learning, the structure of such networks inspired the development of deep filter banks referred to as scattering transforms. These transforms apply a cascade of wavelet transforms and complex modulus operators to extract features that are invariant to group operations and stable to deformations. Furthermore, ConvNets inspired recent advances in geometric deep learning, which aim to generalize these networks to graph data by applying notions from graph signal processing to learn deep graph filter cascades. We further advance these lines of research by proposing a geometric scattering transform using graph wavelets defined in terms of random walks on the graph. We demonstrate the utility of features extracted with this designed deep filter bank in graph classification, and show its competitive performance relative to other methods, including graph kernel methods and geometric deep learning ones, on both social and biochemistry data.
READ FULL TEXT
Comments
There are no comments yet.