Date: Monday, June 24th 2013
Bassam Mokbel and Xibin Zhu,
Cognitive Interaction Technology Center of Excellence (CITEC), Bielefeld University, Bielefeld, Germany
Room: 5161.0267 (Bernoulliborg)
Dimensionality reduction to visualize large data sets
Due to rapidly developing sensor and storage technologies, digital data is available
in large quantities in virtually all academic and industrial disciplines. In order to make
these large (and typically high-dimensional) data sets easily accessible for the respective
field experts, a very active branch of data analysis research is concerned with embed-
ding high-dimensional points as two- or three-dimensional vectors for visualization.
This way, the data set can be displayed as a simple scatter plot, which the user can
inspect visually. Based on a reliable visual representation, human experts can reason
about certain characteristics of the original data, for example by revealing large-scale
structures or investigating close neighborhood relations.
In our talk, we give a brief overview of some well-established dimensionality re-
duction techniques and show examples for real-world applications. We address the
ill-posedness of the task and discuss formal evaluation criteria to assess the reliability
of the embedding. Since many dimensionality reduction methods are based on pairwise
proximities between the data, we will introduce approximation techniques to tackle the
problem of the inherent quadratic complexity of these algorithms.
Colloquium coordinators are Prof.dr. M. Aiello (e-mail :
Prof.dr. M. Biehl (e-mail:
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