Under the title “The terminating-random experiments selector: Fast high-dimensional variable selection with false discovery rate control”, a paper has now been published in Signal Processing, one of the most renowned journals for signal processing. The authors Jasin Machkour, Michael Muma, and Daniel P. Palomar present a method that controls a user-defined false discovery rate (FDR) in large-scale, high-dimensional data and simultaneously maximizes the number of selected variables.

The T-Rex algorithm can deliver reproducible results in significantly less time than benchmark methods. While these require five hours of computation time for variable selection problems with 5,000 variables, the T-Rex selector manages five million variables in the same time.

In general, the T-Rex selector could be of interest for various applications, including medical studies. For example, there is already a collaboration with Charité Berlin to investigate whether the methods are suitable for achieving the same results as conventional methods with less data, i.e. with fewer study participants.

The open-source software package also developed by the researchers has already sparked great interest. It has already been downloaded 16,000 times since it was made publicly available.

In another article, which will soon be published in the Journal Signal Processing, as well as two conference papers, which will be presented in April at the International Conference on Acoustics, Speech, and Signal Processing (IEEE ICASSP), the largest conference for researchers in the field of signal processing, the scientists present further methodological and algorithmic results as well as software solutions on the topic, including an extension to complex-valued data.