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Novel non-parametric dimensionality reduction techniques such as t-distributed stochastic neighbor embedding (t-SNE) lead to a powerful and flexible visualization of high-dimensional data. One drawback of non-parametric techniques is their lack of an explicit out-of-sample extension. In this contribution, we propose an efficient extension of t-SNE to a parametric framework, kernel t-SNE, which preserves...
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...
We investigate the potential of modern nonlinear dimensionality reduction techniques for an interactive cluster detection in bioinformatics applications. We demonstrate that recent non-parametric techniques such as t-distributed stochastic neighbor embedding (t-SNE) allow a cluster identification which is superior to direct clustering of the original data or cluster detection based on classical parametric...
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