72,770 research outputs found
Tensor-Based Algorithms for Image Classification
Interest in machine learning with tensor networks has been growing rapidly in recent years. We show that tensor-based methods developed for learning the governing equations of dynamical systems from data can, in the same way, be used for supervised learning problems and propose two novel approaches for image classification. One is a kernel-based reformulation of the previously introduced multidimensional approximation of nonlinear dynamics (MANDy), the other an alternating ridge regression in the tensor train format. We apply both methods to the MNIST and fashion MNIST data set and show that the approaches are competitive with state-of-the-art neural network-based classifiers
Koopman Operator and its Approximations for Systems with Symmetries
Nonlinear dynamical systems with symmetries exhibit a rich variety of
behaviors, including complex attractor-basin portraits and enhanced and
suppressed bifurcations. Symmetry arguments provide a way to study these
collective behaviors and to simplify their analysis. The Koopman operator is an
infinite dimensional linear operator that fully captures a system's nonlinear
dynamics through the linear evolution of functions of the state space.
Importantly, in contrast with local linearization, it preserves a system's
global nonlinear features. We demonstrate how the presence of symmetries
affects the Koopman operator structure and its spectral properties. In fact, we
show that symmetry considerations can also simplify finding the Koopman
operator approximations using the extended and kernel dynamic mode
decomposition methods (EDMD and kernel DMD). Specifically, representation
theory allows us to demonstrate that an isotypic component basis induces block
diagonal structure in operator approximations, revealing hidden organization.
Practically, if the data is symmetric, the EDMD and kernel DMD methods can be
modified to give more efficient computation of the Koopman operator
approximation and its eigenvalues, eigenfunctions, and eigenmodes. Rounding out
the development, we discuss the effect of measurement noise
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