Affiliations: [a] School of Electrical and Electronic Engineering, University of Science Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia | [b] Faculty of Cognitive Sciences and Human Development, University Malaysia Sarawak, 94300, Kota Samarahan, Sarawak, Malaysia
Abstract: This paper proposes a novel model that integrates the SOM (Self-Organizing Map) neural network and the kMER (kernel-based Maximum Entropy learning Rule) algorithm for data visualization and classification. The rationales and algorithm development of SOM-kMER are elaborated. Applicability of the proposed model is evaluated using a number of simulated and benchmark data sets. The outcomes demonstrate that SOM-kMER is able to achieve a faster convergence rate (as compared with the kMER) and produce visualization with fewer dead units (as compared with the SOM). The proposed SOM-kMER model is also able to form an equiprobabilistic map at the end of its learning process. On benchmark experiments, SOM-kMER achieves favourable results as compared with the SOM and other machine learning algorithms.
Keywords: Topographic map, multivariate data projection, pattern classification, self-organizing map, kernel-based maximum entropy learning rule
