IEICE TRANSACTIONS on Information
L* Learning: A Fast Self-Organizing Feature Map Learning Algorithm Based on Incremental Ordering
Summary :
The self-organizing feature map is one of the most widely used neural network paradigm based on unsupervised competitive learning. However, the learning algorithm introduced by Kohonen is very slow when the size of the map is large. The slowness is caused by the search for large map in each training steps of the learning. In this paper, a fast learning algorithm based on incremental ordering is proposed. The new learning starts with only a few units evenly distributed on a large topological feature map, and gradually increases the number of units until it covers the entire map. In middle phases of the learning, some units are well-ordered and others are not, while all units are weekly-ordered in Kohonen learning. The ordered units, during the learning, help to accelerate the search speed of the algorithm and accelerate the movements of the remaining unordered units to their topological locations. It is shown by theoretical analysis as well as experimental analysis that the proposed learning algorithm reduces the training time from O(M2) to O(log M) for M by M map without any additional working space, while preserving the ordering properties of the Kohonen learning algorithm.
- Publication
- IEICE TRANSACTIONS on Information Vol.E76-D No.6 pp.698-706
- Publication Date
- 1993/06/25
- Publicized
- Online ISSN
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- Type of Manuscript
- PAPER
- Category
- Bio-Cybernetics
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Young Pyo JUN, Hyunsoo YOON, Jung Wan CHO, "L* Learning: A Fast Self-Organizing Feature Map Learning Algorithm Based on Incremental Ordering" in IEICE TRANSACTIONS on Information,
vol. E76-D, no. 6, pp. 698-706, June 1993, doi: .
Abstract: The self-organizing feature map is one of the most widely used neural network paradigm based on unsupervised competitive learning. However, the learning algorithm introduced by Kohonen is very slow when the size of the map is large. The slowness is caused by the search for large map in each training steps of the learning. In this paper, a fast learning algorithm based on incremental ordering is proposed. The new learning starts with only a few units evenly distributed on a large topological feature map, and gradually increases the number of units until it covers the entire map. In middle phases of the learning, some units are well-ordered and others are not, while all units are weekly-ordered in Kohonen learning. The ordered units, during the learning, help to accelerate the search speed of the algorithm and accelerate the movements of the remaining unordered units to their topological locations. It is shown by theoretical analysis as well as experimental analysis that the proposed learning algorithm reduces the training time from O(M2) to O(log M) for M by M map without any additional working space, while preserving the ordering properties of the Kohonen learning algorithm.
URL: https://global.ieice.org/en_transactions/information/10.1587/e76-d_6_698/_p
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@ARTICLE{e76-d_6_698,
author={Young Pyo JUN, Hyunsoo YOON, Jung Wan CHO, },
journal={IEICE TRANSACTIONS on Information},
title={L* Learning: A Fast Self-Organizing Feature Map Learning Algorithm Based on Incremental Ordering},
year={1993},
volume={E76-D},
number={6},
pages={698-706},
abstract={The self-organizing feature map is one of the most widely used neural network paradigm based on unsupervised competitive learning. However, the learning algorithm introduced by Kohonen is very slow when the size of the map is large. The slowness is caused by the search for large map in each training steps of the learning. In this paper, a fast learning algorithm based on incremental ordering is proposed. The new learning starts with only a few units evenly distributed on a large topological feature map, and gradually increases the number of units until it covers the entire map. In middle phases of the learning, some units are well-ordered and others are not, while all units are weekly-ordered in Kohonen learning. The ordered units, during the learning, help to accelerate the search speed of the algorithm and accelerate the movements of the remaining unordered units to their topological locations. It is shown by theoretical analysis as well as experimental analysis that the proposed learning algorithm reduces the training time from O(M2) to O(log M) for M by M map without any additional working space, while preserving the ordering properties of the Kohonen learning algorithm.},
keywords={},
doi={},
ISSN={},
month={June},}
Copy
TY - JOUR
TI - L* Learning: A Fast Self-Organizing Feature Map Learning Algorithm Based on Incremental Ordering
T2 - IEICE TRANSACTIONS on Information
SP - 698
EP - 706
AU - Young Pyo JUN
AU - Hyunsoo YOON
AU - Jung Wan CHO
PY - 1993
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E76-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 1993
AB - The self-organizing feature map is one of the most widely used neural network paradigm based on unsupervised competitive learning. However, the learning algorithm introduced by Kohonen is very slow when the size of the map is large. The slowness is caused by the search for large map in each training steps of the learning. In this paper, a fast learning algorithm based on incremental ordering is proposed. The new learning starts with only a few units evenly distributed on a large topological feature map, and gradually increases the number of units until it covers the entire map. In middle phases of the learning, some units are well-ordered and others are not, while all units are weekly-ordered in Kohonen learning. The ordered units, during the learning, help to accelerate the search speed of the algorithm and accelerate the movements of the remaining unordered units to their topological locations. It is shown by theoretical analysis as well as experimental analysis that the proposed learning algorithm reduces the training time from O(M2) to O(log M) for M by M map without any additional working space, while preserving the ordering properties of the Kohonen learning algorithm.
ER -
English
