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Project

Numerical Tensor Algorithms

Many real-world applications create large data-sets, which are in need for efficient, robust numerical algorithms to process. One recent research topic are algorithms, which decompose such large data into smaller representations, such as the polyadic decomposition. This will not only speed up the computation, but because it reduces the degree of freedom, it will produce more robust results. This is also refereed as avoiding the curse of dimensional.
Date:7 Oct 2019 →  30 Sep 2020
Keywords:Curse of dimensionality, Polyadic decomposition
Disciplines:Signal processing
Project type:PhD project