We present a probabilistic model-based framework for distributed learning that takes into account privacy restrictions and is applicable to scenarios where the different sites ha...
The integration of scalable performance analysis in parallel development tools is difficult. The potential size of data sets and the need to compare results from multiple experime...
Kevin A. Huck, Allen D. Malony, Sameer Shende, Ala...
The increasing popularity of social networks has initiated a fertile research area in information extraction and data mining. Although such analysis can facilitate better understan...
The analysis of high-dimensional data is an important, yet inherently difficult problem. Projection techniques such as PCA, MDS, and SOM can be used to map high-dimensional data t...
Tobias Schreck, Tatiana von Landesberger, Sebastia...
Manifold learning methods are promising data analysis tools. However, if we locate a new test sample on the manifold, we have to find its embedding by making use of the learned e...