HieNet architecture using the K-Iterations Fast Learning artificial Neural Networks
Lester Tay, Jozef M. Zurada, Lai-Ping Wong · 2008
This paper proposes a hierarchical architecture, HieNet, that utilizes the K-Iterations Fast Learning artificial Neural Network (KFLANN). Effective in its clustering capabilities, the KFLANN is capable of providing more stable and consistent clusters that are independent data presentation sequences (DPS). Leveraging on the ability to provide more consistent clusters, the KFLANN is initially used to identify the homogeneous Feature Spaces that prepare large dimensional networks for a hierarchical organization. We illustrate how this hierarchical structure can be constructed through the recurring use of the KFLANN and support our work with experimental results.