Expectation maximization transfer learning and its application for bionic hand prostheses

NEUROCOMPUTING

卷:298

Article

Paassen, Benjamin¹  Schulz, Alexander¹  Hahne, Janne²  Hammer, Barbara¹ 

[1] Bielefeld Univ, Ctr Excellence Cognit Interact Technol CITEC, Machine Learning Grp, Inspirat 1, D-33619 Bielefeld, Germany

[2] Georg August Univ, Appl Surg & Rehabil Technol Lab ART Lab, Dept Trauma Surg, Orthoped Surg & Hand Surg Univ,Med Ctr Gottingen, Von Siebold Str 3, D-37075 Gottingen, Germany

关键词: Transfer learning;    Gaussian mixture models;    Learning vector quantization;    Expectation maximization;    Bionic prostheses;   

DOI  :  10.1016/j.neucom.2017.11.072

来源: Elsevier

【 摘 要 】

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 according to the undisturbed model. 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. (C) 2018 Elsevier B.V. All rights reserved.

【 授权许可】

Free