Advances in dissimilarity-based data visualisation

Gisbrecht A. Advances in dissimilarity-based data visualisation. Bielefeld: Universitätsbibliothek Bielefeld; 2015

Parametric nonlinear dimensionality reduction using kernel t-SNE

Gisbrecht A, Schulz A, Hammer B. Parametric nonlinear dimensionality reduction using kernel t-SNE. Neurocomputing. 2015;147:71-82

Using Discriminative Dimensionality Reduction to Visualize Classifiers

Schulz A, Gisbrecht A, Hammer B. Using Discriminative Dimensionality Reduction to Visualize Classifiers. Neural Processing Letters. 2015;42(1):27-54.Albeit automated classifiers offer a standard tool in many application areas, there exists hardly a generic possibility to directly inspect their behavior, which goes beyond the mere classification of (sets of) data points. In this contribution, we propose a general framework how to visualize a given classifier and its behavior as concerns a given data set in two dimensions. More specifically, we use modern nonlinear dimensionality reduction (DR) techniques to project a given set of data points and their relation to the classification decision boundaries. Furthermore, since data are usually intrinsically more than two-dimensional and hence cannot be projected to two dimensions without information loss, we propose to use discriminative DR methods which shape the projection according to given class labeling as is the case for a classification setting. With a given data set, this framework can be used to visualize any trained classifier which provides a probability or certainty of the classification together with the predicted class label. We demonstrate the suitability of the framework in the context of different dimensionality reduction techniques, in the context of different attention foci as concerns the visualization, and as concerns different classifiers which should be visualized

Data visualization by nonlinear dimensionality reduction

Gisbrecht A, Hammer B. Data visualization by nonlinear dimensionality reduction. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2015;5(2):51-73

Relevance learning in generative topographic mapping

Gisbrecht A, Hammer B. Relevance learning in generative topographic mapping. Neurocomputing. 2011;74(9):1351-1358

Data Analysis of (Non-)Metric Proximities at Linear Costs

Schleif F-M, Gisbrecht A. Data Analysis of (Non-)Metric Proximities at Linear Costs. In: Proceedings of SIMBAD 2013. Berlin, Heidelberg: Springer; 2013: 59-74

On the effect of clustering on quality assessment measures for dimensionality reduction

Mokbel B, Gisbrecht A, Hammer B. On the effect of clustering on quality assessment measures for dimensionality reduction. In: NIPS workshop on Challenges of Data Visualization. 2010

Discriminative Dimensionality Reduction for the Visualization of Classifiers

Gisbrecht A, Schulz A, Hammer B. Discriminative Dimensionality Reduction for the Visualization of Classifiers. In: Pattern Recognition Applications and Methods. Advances in Intelligent Systems and Computing. Vol 318. Cham: Springer Science + Business Media; 2014: 39-56

Efficient Approximations of Kernel Robust Soft LVQ

Hofmann D, Gisbrecht A, Hammer B. Efficient Approximations of Kernel Robust Soft LVQ. In: WSOM. 2012