Speeding up the self-organizing feature map using dynamic subset selection

An active learning algorithm is devised for training Self-Organizing Feature Maps on large data sets. Active learning algorithms recognize that not all exemplars are created equal. Thus, the concepts of exemplar age and difficulty are used to filter the original data set such that training epochs ar...

Full description

Saved in:
Bibliographic Details
Published in:Neural processing letters 2005-08, Vol.22 (1), p.17-32
Main Authors: WETMORE, Leigh, HEYWOOD, Malcolm I, ZINCIR-HEYWOOD, A. Nur
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Connectionism. Neural networks
Datasets
Exact sciences and technology
Feature maps
Information systems. Data bases
Machine learning
Memory organisation. Data processing
Self organizing maps
Software
Unsupervised learning
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:An active learning algorithm is devised for training Self-Organizing Feature Maps on large data sets. Active learning algorithms recognize that not all exemplars are created equal. Thus, the concepts of exemplar age and difficulty are used to filter the original data set such that training epochs are only conducted over a small subset of the original data set. The ensuing Hierarchical Dynamic Subset Selection algorithm introduces definitions for exemplar difficulty suitable to an unsupervised learning context and therefore appropriate Self-organizing map (SOM) stopping criteria. The algorithm is benchmarked on several real world data sets with training set exemplar counts in the region of 30–500 thousand. Cluster accuracy is demonstrated to be at least as good as that from the original SOM algorithm while requiring a fraction of the computational overhead.
ISSN:1370-4621
1573-773X
DOI:10.1007/s11063-004-7775-6