Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/a12110240 | ALGORITHMS |
Keywords | Field | DocType |
quantum machine learning,image classification,tensor train format,kernel-based methods,ridge regression | Kernel (linear algebra),MNIST database,Quantum machine learning,Tensor,Supervised learning,Dynamical systems theory,Artificial intelligence,Contextual image classification,Artificial neural network,Mathematics,Machine learning | Journal |
Volume | Issue | Citations |
12 | 11 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefan Klus | 1 | 17 | 6.09 |
| Patrick Gelß | 2 | 0 | 1.35 |