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Robust classifier training with small data sets

(Suitable for MSc thesis)

While big data fuels the machine learning progress based on deep neural networks, many real-life problems come with limited data sets only. This is specifically the case for some medical domains, where the number of patients to learn from is small and a single recording of a patient is limited to a few hundred data points at most.

Machine learning models trained on and applied for such small data sets can profit from data augmentation and transfer learning. In this project, a novel strategy shall be evaluated for the robustification of covariance matrices, which are the basis for several subspace projection methods such as the linear discriminant analysis (LDA).


  • solid math background, specifically linear algebra
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