Fast Kernel Ridge Regression by matrix approximation techniques

The topic of this project is the efficient training of Machine Learning by Kernel Ridge Regression.

Relevant content will be:

  • Kernel Ridge Regression
  • iterative solvers for linear systems
  • matrix approximation techniques:
    • low rank approximation (SVD, ACA, …)
    • Askit
    • Hierarchical Matrices

Application data should be large-scale and science-related. Maybe the first starting point would be data from quantum chemistry that I have access to.
The beauty of this project would be to further develop and analyze the impact of non-exact solvers for linear systems on the quality of the prediction of Kernel Ridge Regression. This is highly research relevant.

WARNING: Some flavor of this topic (e.g. hierarchical matrices) requires a profound mathematical background.

Some first links: