TY - GEN
T1 - Networking Self-Organising Maps and Similarity Weight Associations
AU - Chung, Younjin
AU - Gudmundsson, Joachim
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Using a Self-Organising Map (SOM), the structure of a data set can be explored when analysing patterns between data that are multivariate, nonlinear and unlabelled in nature. As a SOM alone cannot be used to explore patterns between different data sets, a similarity weighting scheme was previously introduced to associate different SOMs in a network fashion and approximate output patterns for given inputs. This approach uses a global weight association method on the combination of all SOMs specified for a network. However, there has been a difficulty in defining the association when changing the SOM network structure. Furthermore, it has always produced the same output weight distribution for different input data that have the same best matching unit. In an attempt to overcome the issues, we propose a new approach in this paper for locally associating a pair of SOMs as a basic network building block and approximating individually associated weight distribution. The experiments using ecological data demonstrate that the proposed approach effectively associates a pair of input and output SOMs for structural flexibility of the SOM network with better approximation of output weight distributions for individual input data.
AB - Using a Self-Organising Map (SOM), the structure of a data set can be explored when analysing patterns between data that are multivariate, nonlinear and unlabelled in nature. As a SOM alone cannot be used to explore patterns between different data sets, a similarity weighting scheme was previously introduced to associate different SOMs in a network fashion and approximate output patterns for given inputs. This approach uses a global weight association method on the combination of all SOMs specified for a network. However, there has been a difficulty in defining the association when changing the SOM network structure. Furthermore, it has always produced the same output weight distribution for different input data that have the same best matching unit. In an attempt to overcome the issues, we propose a new approach in this paper for locally associating a pair of SOMs as a basic network building block and approximating individually associated weight distribution. The experiments using ecological data demonstrate that the proposed approach effectively associates a pair of input and output SOMs for structural flexibility of the SOM network with better approximation of output weight distributions for individual input data.
KW - Input and output
KW - Network
KW - Pattern analysis
KW - Self-Organizing Map
KW - Similarity weight measure
KW - Weight association
UR - http://www.scopus.com/inward/record.url?scp=85078431660&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-36802-9_82
DO - 10.1007/978-3-030-36802-9_82
M3 - Conference contribution
SN - 9783030368012
T3 - Communications in Computer and Information Science
SP - 779
EP - 788
BT - Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings
A2 - Gedeon, Tom
A2 - Wong, Kok Wai
A2 - Lee, Minho
PB - Springer
T2 - 26th International Conference on Neural Information Processing, ICONIP 2019
Y2 - 12 December 2019 through 15 December 2019
ER -