Adaptive density estimation based on self-organizing incremental neural network using Gaussian process
Xiaoyu Wang, Osamu Hasegawa · 2017
This paper firstly analyzes the shortcoming of a self-organizing incremental neural network (SOINN), then proposes a novel online similarity metric and online adaptive kernel density estimator to handle 2 basic problems of unsupervised learning: clustering and density estimation. Our approach is an extension of the standard Gaussian process, online density estimator and SOINN; not only does it fully consider the local structure (the local distribution around each node), but also the learning of parameters is adjusted to make it feasible to online learning. The experiment results show that our method is more accurate and robust to multiscale, multimodal and multivariate data.