Share Email Print

Proceedings Paper

Parallel distributed RSOM tree for pattern classification
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Data clustering requires high-performance computers to get results in a reasonable amount of time, particularly for large-scale databases. A feasible approach to reduce processing time is to implement on scalable parallel computers. Thus, RSOM tree method is proposed. Firstly a SOM net, as the root node, is trained. Then, all trained samples are allocated to the output nodes of the root node according to WTA-criterion. Thirdly, the parameters of discriminability are calculated form the samples for each node. If discriminable, the node will be SOM-split and labeled as an internal node, otherwise an end node, and the split terminates. Recursively check or split all nodes until there is no node meeting with the discrimination criteria. Finally, a RSOM tree is obtained. In this process, several kinds of control-factors, e.g. inter-class and intra-class discrimination criteria, layer number, sample number, and correct ratio of classification, are obtained from the data in each node. Accordingly the good choice of the RSOM structure can be obtained, and the generalization capability is assured. This RSOM tree method is of the nature of parallelism, and can be implemented on scalable parallel computers, including high performance Cluster-computers, and local or global computer networks. The former becomes more and more attractive except for its expensiveness, while the latter is much more economic rewarding, and might belong to Grid- Computation to a great extend. Based on the above two kinds of hardware systems, the performance of this method is tested with the large feature data sets which are extracted from a large amount of video pictures.

Paper Details

Date Published: 17 April 2006
PDF: 12 pages
Proc. SPIE 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV, 624712 (17 April 2006); doi: 10.1117/12.664760
Show Author Affiliations
Shengping Xia, National Univ. of Defense Technology (China)
Weidong Hu, National Univ. of Defense Technology (China)

Published in SPIE Proceedings Vol. 6247:
Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV
Harold H. Szu, Editor(s)

© SPIE. Terms of Use
Back to Top