Deep Haar Scattering Networks

Xiuyuan Cheng; Xu Chen; Stephane Mallat

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arXiv · 1509.09187

Deep Haar Scattering Networks

Xiuyuan Cheng·Xu Chen·Stephane Mallat

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Abstract

An orthogonal Haar scattering transform is a deep network, computed with a hierarchy of additions, subtractions and absolute values, over pairs of coefficients. It provides a simple mathematical model for unsupervised deep network learning. It implements non-linear contractions, which are optimized for classification, with an unsupervised pair matching algorithm, of polynomial complexity. A structured Haar scattering over graph data computes permutation invariant representations of groups of connected points in the graph. If the graph connectivity is unknown, unsupervised Haar pair learning can provide a consistent estimation of connected dyadic groups of points. Classification results are given on image data bases, defined on regular grids or graphs, with a connectivity which may be known or unknown.

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Xiuyuan Cheng, Xu Chen, Stephane Mallat. 2015-09-30. Deep Haar Scattering Networks. https://arxiv.org/abs/1509.09187

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