Classification by Invariant Scattering
Stephane Mallat
(Ecole Polytechnique)
*Joint Applied Math Seminar/Harmonic Analysis and Signal
Processing
Seminar*
Signal classes are usually invariant to groups of operators such as
translations or scalings, and to larger Lie groups of deformations.
Classification thus requires finding informative invariants.
Invariants are also at the core of quantum
physics through Gauge theories. We introduce non-linear
invariant operators, similar to quantum scattering. These operators
have small commutators with elastic deformations and
provide new representations of stationary processes.
Their computational architecture reminds deep neural networks, but
learning
is needed at a single layer, and implemented with O(N) operations.
State of the art results are shown for image classification
of deformed patterns and random textures.