Parallel implementation of multidimensional scaling algorithm based on particle dynamics

Authors:
Piotr Pawliczek;Witold Dzwinel
Affiliations:
AGH University of Science and Technology, Institute of Computer Science, Krakow, Poland;AGH University of Science and Technology, Institute of Computer Science, Krakow, Poland and WSEiP, College of Economy and Law, Department of Computer Science, Kielce, Poland
Venue:
PPAM'09 Proceedings of the 8th international conference on Parallel processing and applied mathematics: Part I
Year:
2009

Citing 3
Cited 0

Virtual particles and search for global minimum

Future Generation Computer Systems - Special issue on HPCN96
Parallel molecular dynamics: communication requirements for massively parallel machines

FRONTIERS '95 Proceedings of the Fifth Symposium on the Frontiers of Massively Parallel Computation (Frontiers'95)
A Nonlinear Mapping for Data Structure Analysis

IEEE Transactions on Computers

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose here a parallel implementation of multidimensional scaling (MDS) method which can be used for visualization of large datasets of multidimensional data. Unlike in traditional approaches, which employ classical minimization methods for finding the global optimum of the "stress function", we use a heuristic based on particle dynamics. This method allows avoiding local minima and is convergent to the global one. However, due to its O(N2) complexity, the application of this method in data mining problems involving large datasets requires efficient parallel codes. We show that employing both optimized Taylor's algorithm and hybridized model of parallel computations, our solver is efficient enough to visualize multidimensional data sets consisting of 104 feature vectors in time of minutes.