Integrating the improved CBP model with kernel SOM

Abstract

In this paper, we first design a more generalized network model, Improved circular back propagation (CBP), based on the same structure as CBP proposed by Ridella et al. The novelty of Improved circular back propagation neural network (ICBP) lies in: (1) it substitutes the original extra added node with the isotropic quadratic form input in CBP with the one with an anisotropic quadratic form input; (2) particularly, the weights between the extra node and all the hidden nodes are endowed fixed values instead of the original changeable values. As a result, ICBP possesses better generalization and adaptability although it has less adjustable weights compared to CBP. Secondly, we propose a new kernel-based self-organizing maps (KSOM) algorithm using the kernel method. Our main motives of using the kernel method are (1) to induce a class of robust non-Euclidean distance measures for the original input space and establish a new objective functions for self-organizing maps (SOM), and thus make the newly established SOM able to cluster the non-Euclidean structures in data; (2) to enhance robustness of the SOM algorithms to noise and outliers and at the same time still retain computational simplicity. And then, with the combined BP-SOM idea of Weijters, we construct a new integrated network ICBP–KSOM. Our motivation of presenting the integration is to construct a high-performance classifier by utilizing both ICBP's good generalization and adaptability and KSOM's higher classification performance and robustness comparing to SOM. Finally, the experimental results on three benchmark data sets show the superiority and effectiveness of our new integration in terms of the t-test.