Iterative Linear Algebraic Data Mining Techniques Workshop
Modern relational datasets have rich topology and network scientists seek topological understanding. Recent research efforts propose linear algebraic techniques to aide in classifying, ranking, and clustering data entities. For a large dataset, iterative methods are employed to approximate solutions of algebraic equations. These solutions are in turn used to make algorithmic decisions, allowing further analysis to focus on small subsets of the large dataset. Numerical analysis that is aimed at iterative solvers in this context is a blooming research area. This workshop focuses on numerical analysis issues associated with linear algebraic approaches to data and graph analysis.
List of topics:
Solvers and preconditioning in graphs
Matrix functions for graph analysis
Ranking via linear solves
Impact of skewed degree distribution
Analysis of randomized algorithms
Analysis of convergence tolerances and their relationship to data mining quality
Early stopping criteria
Tutorial: Iterative Methods for Data Analysis
Sunday March 20, 2016
Iterative Methods for Data Analysis
by Van Emden Henson
Sunday 1pm tutorial, 3-4 hours (aimed at graduate-level research assistants in numerical analysis).
Starting Monday March 21, 2016, possibly continuing on Tuesday March 22
Times to be announced
Parallel session on Numerical Analysis of Iterative Linear Algebraic Data
Links to submitted abstracts self-identified as part of this thematic track
will come here once available.
Please submit abstract and select the 'workshop' radio button at
the submission link.
Length of presenations is yet to be announced.