A faster dynamic convergency approach for self-organizing maps
Yükleniyor...
Dosyalar
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
SPRINGER HEIDELBERG
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
This paper proposes a novel variable learning rate to address two main challenges of the conventional Self-Organizing Maps (SOM) termed VLRSOM: high accuracy with fast convergence and low topological error. We empirically showed that the proposed method exhibits faster convergence behavior. It is also more robust in topology preservation as it maintains an optimal topology until the end of the maximum iterations. Since the learning rate adaption and the misadjustment parameter depends on the calculated error, the VLRSOM will avoid the undesired results by exploiting the error response during the weight updation. Then the learning rate is updated adaptively after the random initialization at the beginning of the training process. Experimental results show that it eliminates the tradeoff between the rate of convergence and accuracy and maintains the data's topological relationship. Extensive experiments were conducted on different types of datasets to evaluate the performance of the proposed method. First, we experimented with synthetic data and handwritten digits. For each data set, two experiments with a different number of iterations (200 and 500) were performed to test the stability of the network. The proposed method was further evaluated using four benchmark data sets. These datasets include Balance, Wisconsin Breast, Dermatology, and Ionosphere. In addition, a comprehensive comparative analysis was performed between the proposed method and three other SOM techniques: conventional SOM, parameter-less self-organizing map (PLSOM2), and RA-SOM in terms of accuracy, quantization error (QE), and topology error (TE). The results indicated the proposed approach produced superior results to the other three methods.
Açıklama
Anahtar Kelimeler
Self-Organizing Maps, Variable Learning Rate SOM, Quantization Error, Clustering, Dimensionality Reduction
Kaynak
COMPLEX & INTELLIGENT SYSTEMS
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
Sayı
Künye
Jamil, A., Hameed, A. A., Orman, Z. (2022). A faster dynamic convergency approach for self-organizing maps. Complex & Intelligent Systems.
