Unsupervised Deep Haar Scattering on Graphs
Machine Learning
2014-11-04 v2 Computer Vision and Pattern Recognition
Abstract
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. Supervised classification with dimension reduction is tested on data bases of scrambled images, and for signals sampled on unknown irregular grids on a sphere.
Keywords
Cite
@article{arxiv.1406.2390,
title = {Unsupervised Deep Haar Scattering on Graphs},
author = {Xu Chen and Xiuyuan Cheng and Stéphane Mallat},
journal= {arXiv preprint arXiv:1406.2390},
year = {2014}
}