Expectation maximization transfer learning and its application for bionic hand prostheses

TL;DR

This paper introduces a novel expectation maximization transfer learning algorithm that learns linear transformations to improve classification accuracy in bionic hand prostheses, especially with limited data or classes.

Contribution

The paper proposes a new EM-based transfer learning method for linear transformations, applicable to discriminative models, enhancing robustness in bionic prosthesis data.

Findings

Improves classification accuracy significantly in bionic prostheses data.

Outperforms baseline methods with limited data or classes.

Generalizes to discriminative models like learning vector quantization.

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.