Progressive Learning Paradigms using the Parallel K-Iterations Fast Learning Artificial Neural Network

This paper illustrates how progressive network learning can be performed using concepts from the K-iterations fast learning artificial neural network (KFLANN). It is common in certain application domains to require knowledge updating of existing neural networks and many existing learning paradigms require extensive re-training. The KFLANN is an efficient clustering algorithm that provides consistent clusters within a short number of epochs and the paper explains how its capabilities in efficient and consistent clustering aid progressive learning. Progressive learning is useful in domains that handle data samples that arrive at periodic stages over stages of time. The bioinformatics industry is one such domain where data often arrives as segmented blocks during the various stages of bio-molecular experimentation. This work provides an option for progressive learning of partial data that subsequently updates as more complete information is made available. The paper discusses how FLANN, a subset of the KFLANN, can be configured as a progressive learning algorithm to merge segmented data. It further compares the results with those obtained from the typical non-segmented processing. In the process of introducing the progressive learning paradigm in using FLANN, we also present a feasible parallelization algorithm known as the parallel-KFLANN (P-KFLANN).