Hierarchical Fast Learning Artificial Neural Network
Wong Lai Ping, Alex Leng Phuan Tay, Leng Phuan, Jian Xu · 2005
The Hierarchical Fast Learning Artificial Neural Network (HieFLANN) is proposed as an unsupervised learning model that incorporates a hierarchical approach to address pattern classification for high dimensional data. It utilizes K- Means Fast Learning Artificial Neural Network (KFLANN) subnets and a Canonical Covariance Feature Compression (C2FeCom) process. The embedded individual KFLANN subnet autonomously derives the essential localized network parameters from the input data and in the process, builds a hierarchical network. The C2FeCom feature compression process extracts the independent parameters in compact representations from subnets. The proposed algorithm is experimentally evaluated using benchmark datasets.