Unsupervised Deep Haar Scattering on Graphs

Understand

The classification of high-dimensional data defined on graphs is particularly difficult when the graph geometry is unknown.

• We introduce a Haar scattering transform on graphs, which computes invariant signal descriptors.
• It is implemented with a deep cascade of additions, subtractions and absolute values, which iteratively compute orthogonal Haar wavelet transforms.
• Multiscale neighborhoods of unknown graphs are estimated by minimizing an average total variation, with a pair matching algorithm of polynomial complexity.