Factorization of broad expansion for broad learning system

The broad learning system (BLS) based on the random vector functional link neural network is a new versatile non-iterative neural network for rapidly selecting models. One of its fascinating features is the broad expansion, a dynamic adjustment strategy in the changing environment. However, the added enhancement nodes often suffer from information redundancy and loss, a failure in the broad expansion. Therefore, the factorization of enhancement nodes, which makes each code independent of others and achieves minimal redundancy and loss representation, is critical to successful broad expansion. Inspired by this idea, in this study, a factorial broad expansion (FBE) strategy is proposed to aid BLS (FBE-BLS) during broad expansion. The strategy is proposed as a component for reducing the information redundancy and loss in the classical BLS. This strategy is an adversarial framework to learn the factorial codes at the enhancement layer. The factorial and predictor networks adversarially learn factorial codes by chasing independence, minimizing predictability among enhancement nodes. In experiments, considering that few works focused on information redundancy and loss in the broad expansion of BLS, the FBE-BLS is compared with the classical BLS, and state-of-the-art methods are selected. The results of FBE-BLS on UCI and real-world image data are very promising, exhibiting higher accuracy during broad expansion.