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Expectation maximization transfer learning and its application for bionic hand prostheses

Published on Feb 1, 2018in Neurotoxicology3.26
· DOI :10.1016/j.neucom.2017.11.072
Benjamin Paaßen7
Estimated H-index: 7
(Bielefeld University),
Alexander Schulz38
Estimated H-index: 38
(Bielefeld University)
+ 1 AuthorsBarbara Hammer33
Estimated H-index: 33
(Bielefeld University)
Abstract
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 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.
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  • References (23)
  • Citations (3)
References23
Newest
#1Cosima Prahm (Medical University of Vienna)H-Index: 5
#2Benjamin Paaßen (Citec)H-Index: 7
Last.Oskar C. Aszmann (Medical University of Vienna)H-Index: 25
view all 5 authors...
Dec 1, 2016 in ICDM (International Conference on Data Mining)
#1Viktor Losing (Bielefeld University)H-Index: 5
#2Barbara Hammer (Bielefeld University)H-Index: 33
Last.Heiko Wersing (Honda)H-Index: 19
view all 3 authors...
#1Gregory Ditzler (UA: University of Arizona)H-Index: 10
#2Manuel Roveri (Polytechnic University of Milan)H-Index: 19
Last.Robi Polikar (Rowan University)H-Index: 30
view all 4 authors...
#1Patrick BlöbaumH-Index: 1
#2Alexander Schulz (Bielefeld University)H-Index: 38
Last.Barbara Hammer (Bielefeld University)H-Index: 33
view all 3 authors...
#1Dario Farina (GAU: University of Göttingen)H-Index: 76
#2Ning Jiang (GAU: University of Göttingen)H-Index: 29
Last.Oskar C. Aszmann (Medical University of Vienna)H-Index: 25
view all 7 authors...
#1Rami N. Khushaba (UTS: University of Technology, Sydney)H-Index: 19
#2Maen Takruri (American University of Ras Al Khaimah)H-Index: 9
Last.Sarath Kodagoda (UTS: University of Technology, Sydney)H-Index: 18
view all 4 authors...
#1David Barber (UCL: University College London)H-Index: 23
Cited By3
Newest
#1Nikolaos Passalis (A.U.Th.: Aristotle University of Thessaloniki)H-Index: 9
#2Anastasios Tefas (A.U.Th.: Aristotle University of Thessaloniki)H-Index: 30
#1Alexander Schulz (Citec)H-Index: 38
#2Jeffrey Frederic Queiber (Bielefeld University)
Last.Minoru Asada (Osaka University)H-Index: 38
view all 4 authors...
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