Expectation maximization transfer learning and its application for bionic hand prostheses

Paaßen B, Schulz A, Hahne J, Hammer B (In Press)
Neurocomputing.

Download
No fulltext has been uploaded. References only!
Journal Article | Original Article | In Press | English

No fulltext has been uploaded

Abstract
Machine learning models in practical settings are typically confronted with changes to the distribution of the incoming data. Such changes can severely affect the model performance, leading for example to misclassifications of data. This is particularly apparent in the domain of bionic hand prostheses, where machine learning models promise faster and more intuitive user interfaces, but are hindered by their lack of robustness to everyday disturbances, such as electrode shifts. One way to address changes in the data distribution is transfer learning, that is, to transfer the disturbed data to a space where the original model is applicable again. In this contribution, we propose a novel expectation maximization algorithm to learn linear transformations that maximize the likelihood of disturbed data after the transformation. We also show that this approach generalizes to discriminative models, in particular learning vector quantization models. In our evaluation on data from the bionic prostheses domain we demonstrate that our approach can learn a transformation which improves classification accuracy significantly and outperforms all tested baselines, if few data or few classes are available in the target domain.
Publishing Year
PUB-ID

Cite this

Paaßen B, Schulz A, Hahne J, Hammer B. Expectation maximization transfer learning and its application for bionic hand prostheses. Neurocomputing. In Press.
Paaßen, B., Schulz, A., Hahne, J., & Hammer, B. (In Press). Expectation maximization transfer learning and its application for bionic hand prostheses. Neurocomputing
Paaßen, B., Schulz, A., Hahne, J., and Hammer, B. (In Press). Expectation maximization transfer learning and its application for bionic hand prostheses. Neurocomputing.
Paaßen, B., et al., In Press. Expectation maximization transfer learning and its application for bionic hand prostheses. Neurocomputing.
B. Paaßen, et al., “Expectation maximization transfer learning and its application for bionic hand prostheses”, Neurocomputing, In Press.
Paaßen, B., Schulz, A., Hahne, J., Hammer, B.: Expectation maximization transfer learning and its application for bionic hand prostheses. Neurocomputing. (In Press).
Paaßen, Benjamin, Schulz, Alexander, Hahne, Janne, and Hammer, Barbara. “Expectation maximization transfer learning and its application for bionic hand prostheses”. Neurocomputing (In Press).
This data publication is cited in the following publications:
This publication cites the following data publications:
2912671
Linear Supervised Transfer Learning Toolbox
Paaßen B, Schulz A (2017) : Bielefeld University. doi:10.4119/unibi/2912671.
Material in PUB:
Earlier Version
An EM transfer learning algorithm with applications in bionic hand prostheses
Paaßen B, Schulz A, Hahne J, Hammer B (2017)
In: Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017). Verleysen M (Ed); Bruges: i6doc.com: 129-134.

Export

0 Marked Publications

Open Data PUB

Sources

arXiv 1711.09256

Search this title in

Google Scholar