A data partition method for parallel self-organizing map

Author

Yang, Ming-Hsuan ; Ahuja, Narendra

Author_Institution

Dept. of Comput. Sci., Illinois Univ., Urbana, IL, USA

Volume

3

fYear

1999

fDate

1999

Firstpage

1929

Abstract

We propose a method to partition training vectors into clusters for a parallel implementation of self-organizing map (SOM) algorithm. The proposed algorithm assigns a cluster to a processor such that, in updating weights, the neighbourhoods of a winning node in a cluster do not overlap the neighboring nodes of some winning nodes in other clusters. It reduces the overheads caused by synchronization (i.e., maintaining coherency) of the weight matrices in the processors since the proposed algorithm allows multiple vectors to find their winning nodes and update weights in parallel. Our experimental results show that an average speedup of 3.15 for a parallel implementation of a four processor simulation

Keywords

image coding; learning (artificial intelligence); parallel processing; self-organising feature maps; synchronisation; cluster; data partition; image coding; learning vectors; parallel self-organizing map; synchronization; weight matrices; Clustering algorithms; Computational modeling; Computer science; Computer simulation; Computer vision; Image coding; Image retrieval; Information retrieval; Partitioning algorithms; Web pages;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.832677

Filename

832677