2014
NIPS
NeurIPS 2014
Unsupervised Deep Haar Scattering on Graphs
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.
🌉
Interdisciplinary Bridge
— Deep Learning and Machine Learning
🧭
Keyword Pioneer
— haar scattering transform
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Deep Learning, Interdisciplinary, Machine Learning, Mathematics & Optimization, Speech & Audio
📈
Trend Setter
— Graph Neural Networks
🐣
Hot Topic Early Bird
— graph classification
Authors
Topics
Machine Learning > Core Methods > Representation Learning
Machine Learning > Learning Types > Unsupervised Learning
Deep Learning > Architectures > Neural Networks
Computer Science > Foundations > Algorithms
Machine Learning > Core Methods > Graph Neural Networks
Computer Vision > Processing > Image Processing
Keywords
unsupervised learning
signal classification
graph classification
wavelet transform
deep learning
unsupervised classification
graph signal processing
haar scattering transform
signal descriptors
multiscale neighborhoods
haar wavelet
graph signal
scattering transform
graph neural network
haar scattering