A Kernel Bayesian Adaptive Resonance Theory with A Topological Structure

This paper attempts to solve the typical problems of self-organizing growing network models, i.e. (a) an influence of the order of input data on the self-organizing ability, (b) an instability to high-dimensional data and an excessive sensitivity to noise, and (c) an expensive computational cost by integrating Kernel Bayes Rule (KBR) and Correntropy-Induced Metric (CIM) into Adaptive Resonance Theory (ART) framework. KBR performs a covariance-free Bayesian computation which is able to maintain a fast and stable computation. CIM is a generalized similarity measurement which can maintain a high-noise reduction ability even in a high-dimensional space. In addition, a Growing Neural Gas (GNG)-based topology construction process is integrated into the ART framework to enhance its self-organizing ability. The simulation experiments with synthetic and real-world datasets show that the proposed model has an outstanding stable self-organizing ability for various test environments.

@Article{MLW19,
 	 author =  {Masuyama, Naoki and Loo, Chu Kiong and Wermter, Stefan},
 	 title = {A Kernel Bayesian Adaptive Resonance Theory with A Topological Structure},
 	 booktitle = {None},
 	 journal = {International Journal of Neural Systems},
 	 editors = {None},
 	 number = {5},
 	 volume = {29},
 	 pages = {1--20},
 	 year = {2019},
 	 month = {Jun},
 	 publisher = {World Scientific Publishing},
 	 doi = {10.1142/S0129065718500521},
 }