Large data real-time classification with Non-negative Matrix Factorization and Self-Organizing Maps on GPU
Dept. of Comput. Sci., VSSB-Tech. Univ. of Ostrava, Ostrava, Czech Republic
International Conference on Computer Information Systems and Industrial Management Applications (CISIM), 2010
@conference{platos2010large,
title={Large data real-time classification with Non-negative Matrix Factorization and Self-Organizing Maps on GPU},
author={Platos, J. and Gajdos, P.},
booktitle={Computer Information Systems and Industrial Management Applications (CISIM), 2010 International Conference on},
pages={176–181},
organization={IEEE}
}
This article brings an interesting comparison of two different methods, which were implemented on GPU and help us to detect system intrusions. Generally, both of them can be widely used in the area of information retrieval. The modern trends of parallel computation have a significant influence on performance of implemented methods (Non-negative Matrix Factorization (NMF) and Self-Organizing Maps (SOM)). Both methods were compared on real data.
May 3, 2011 by hgpu
