31 research outputs found
Efficient Kernelization of Discriminative Dimensionality Reduction
Schulz A, Brinkrolf J, Hammer B. Efficient Kernelization of Discriminative Dimensionality Reduction. Neurocomputing. 2017;268(SI):34-41.Modern nonlinear dimensionality reduction (DR) techniques project
high dimensional data to low dimensions for their visual inspection.
Provided the intrinsic data dimensionality is larger than two, DR nec-
essarily faces information loss and the problem becomes ill-posed. Dis-
criminative dimensionality reduction (DiDi) offers one intuitive way
to reduce this ambiguity: it allows a practitioner to identify what is
relevant and what should be regarded as noise by means of intuitive
auxiliary information such as class labels. One powerful DiDi method
relies on a change of the data metric based on the Fisher information.
This technique has been presented for vectorial data so far. The aim
of this contribution is to extend the technique to more general data
structures which are characterised in terms of pairwise similarities only
by means of a kernelisation. We demonstrate that a computation of
the Fisher metric is possible in kernel space, and that it can efficiently
be integrated into modern DR technologies such as t-SNE or faster
Barnes-Hut-SNE. We demonstrate the performance of the approach
in a variety of benchmarks
Differential privacy for learning vector quantization
Brinkrolf J, Göpfert C, Hammer B. Differential privacy for learning vector quantization. Neurocomputing. 2019;342:125-136
Next-generation sequencing of the CHO cell transcriptome
Meeting abstract from 22nd European Society for Animal Cell Technology(ESACT) Meeting on Cell Based Technologies Vienna, Austria. 15-18 May 2011(VLID)90656
Learning Vector Quantization for the Real-World: Privacy, Robustness, and Sparsity
Brinkrolf J. Learning Vector Quantization for the Real-World: Privacy, Robustness, and Sparsity. Bielefeld: Universität Bielefeld; 2023.Machine Learning (ML) methods are increasingly used and outperform humans in many specified and well-defined tasks. Considerable research focuses on optimizing the performance of such methodologies. However, the nature of application areas poses further challenges. For example, in critical domains, a false model behavior poses the risk of fatal mistakes. This is particularly relevant in traffic or medicine. In the latter, the data frequently contains sensitive information which should be preserved. Further, much data are recorded on distributed devices with limited computational power, like smartphones and peripheral devices. Hence, models of low complexity are required. As due to technical, legal, or strategic constraints, data transfer is limited employing intelligent mechanisms is crucial. This requires the consideration of further aspects beyond mere accuracy, namely privacy, robustness, efficiency, and distribution of the data itself.
In this thesis, I address these additional aspects, namely privacy, robustness, efficiency, and distribution of the data for prototype-based classifiers. In particular, I focus on Generalized Learning Vector Quantization (GLVQ) models and their variation to metric adaptations.
I show that the original GLVQ model bears the risk of revealing private information of samples present during the training. I propose three versions of training schemes provably obeying privacy. Further, I propose a novel reject option scheme for GLVQ models. Thereby increasing the robustness of the model is achieved. To reduce the complexity of a model and obtain a sparse representation of feature vectors, I apply regularization to the GLVQ scheme. Finally, I propose a methodology fusing model parameters of several models trained on distributed data sets
Interpretable Machine Learning with Reject Option
Brinkrolf J, Hammer B. Interpretable Machine Learning with Reject Option. at - Automatisierungstechnik. 2018;66(4):283-290
Time integration and reject options for probabilistic output of pairwise LVQ
Brinkrolf J, Hammer B. Time integration and reject options for probabilistic output of pairwise LVQ. Neural Computing and Applications. 2019
Probabilistic extension and reject options for pairwise LVQ
Brinkrolf J, Hammer B. Probabilistic extension and reject options for pairwise LVQ. In: 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM). Piscataway, NJ: IEEE; 2017
Sparse Metric Learning in Prototype-based Classification
Brinkrolf J, Hammer B. Sparse Metric Learning in Prototype-based Classification. In: Verleysen M, ed. Proceedings of the ESANN, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 2020: 375-380
Robust Feature Selection and Robust Training to Cope with Hyperspectral Sensor Shifts
Vaquet V, Brinkrolf J, Hammer B. Robust Feature Selection and Robust Training to Cope with Hyperspectral Sensor Shifts
Differential Privacy for Learning Vector Quantization
Brinkrolf J, Berger K, Hammer B. Differential Privacy for Learning Vector Quantization. In: New Challenges in Neural Computation. 2017