Classification with Invariant Scattering Representations

Joan Bruna; Stéphane Mallat

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arXiv · 1112.1120

Classification with Invariant Scattering Representations

Joan Bruna·Stéphane Mallat

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Abstract

A scattering transform defines a signal representation which is invariant to translations and Lipschitz continuous relatively to deformations. It is implemented with a non-linear convolution network that iterates over wavelet and modulus operators. Lipschitz continuity locally linearizes deformations. Complex classes of signals and textures can be modeled with low-dimensional affine spaces, computed with a PCA in the scattering domain. Classification is performed with a penalized model selection. State of the art results are obtained for handwritten digit recognition over small training sets, and for texture classification.

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Joan Bruna, Stéphane Mallat. 2011-12-05. Classification with Invariant Scattering Representations. https://arxiv.org/abs/1112.1120

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