Fast Topological Adaptive Resonance Theory Based on Correntropy Induced Metric
Naoki Masuyama, Narito Amako, Yusuke Nojima, Yiping Liu, Chu Kiong Loo, Hisao Ishibuchi · 2019
Adaptive Resonance Theory (ART)-based growing self-organizing clustering is one of the most promising approaches for unsupervised topological clustering. In our previous study, we proposed a Topological Correntropy induced metric based ART (TCA) and shown its superior performance. However, TCA suffers from a data-dependent parameter and a complicated network creation process which lead to inefficient learning. This paper aims to solve problems of TCA by implementing an automatic parameter specification mechanism and simplifying a learning algorithm. Experimental results show that the proposed algorithm in this paper successfully solved the above problems.