SC²S Colloquium - April 06, 2016

Date:	April 6, 2016
Room:	02.07.023
Time:	3:00 pm, s.t.

Adrian Sieler: Refinement and Coarsening of Online-Offline Data Mining Methods with Sparse Grids

Due to the Cholesky decomposition and modifications we build algorithms to tackle the problem of adaptivity in a sparse grid density estimation approach. In a framework of an Offline/Online splitting we are now able to modify the underlying Cholesky factorization of the system matrix if the grid is refined or coarsened. Thus, the cost of an Offline step may be drastically reduced, since a new factorization does not need to be applied. With introducing the Cholesky factorization and related modifications, we enlarged the list of possible decomposition methods in the Offline step and provide a full theory and implementation to perform any kind of grid changes. We embedded this methods into a data-stream based density estimation and consequently into a data stream-based classifier as well. Directly after a new data batch is processed and the density declaring coefficients are obtained in the corresponding Online step, we can include the new knowledge into the underlying sparse grid(s). After the learning is done via problem adjusted coarsening and refinement, the changes are induced into the Cholesky factor of the system matrix through our introduced algorithms.