Learning over high dimensional data streams

High dimensional data streams are collected in many scientific projects, humanity research, business processes, social media and the Web.
The challenges of data stream mining are aggravated in high dimensional data, since we have to decide with one single look at the data also about the dimensions that are relevant for the data mining models.
In this talk we will discuss about learning over high dimensional

i) numerical and
ii) textual streams.

Although both cases refer to high dimensional data streams, in

(i) the feature space is fixed, that is, all dimensions are present at each timepoint, whereas in

(ii) the feature space is also evolving as new words show up and old words get out of use.

Bio

Eirini Ntoutsi is an Associate Professor of Intelligent Systems at the Faculty of Electrical Engineering and Computer Science, Leibniz University Hannover, since March 2016. Her research lies in the areas of Data Mining, Machine Learning and Data Science and can be summarized as learning over complex data and data streams.

Prior to joining LUH, she was a postdoctoral researcher at the Ludwig-Maximilians-University (LMU) in Munich, Germany under the supervision of Prof. Hans-Peter Kriegel. She joined LMU in 2010 with an Alexander von Humboldt Foundation fellowship.

She received her PhD in data mining from the University of Piraeus, Greece under the supervision of Prof. Yannis Theodoridis.