Broad learning system: A new learning paradigm and system without going deep

Abstract

This paper introduces a Broad Learning System that gives a new paradigm and learning system without the need of deep architecture. In deep structure and learning, the abundant connecting parameters in filters and layers lead to a time-consuming training process. Broad Learning system, which is established as a flat network, maps the original inputs as mapped features in feature nodes and the structure is expanded in wide sense in the enhancement nodes. Model construction and learning algorithms are introduced here. Moreover, different approaches for the construction of enhancement nodes are given. The advantage of the Broad Learning System is that the learning can be updated dynamically and incrementally without going through a retraining process if the model deems to be expanded on additional feature nodes and enhancement nodes such that the learning is so efficient and effective. In addition, The incremental learning algorithms can be conveniently implemented for fast remodeling in broad expansion which can be referred in [1]. Compared with existing deep neural networks, experimental results on the MNIST data manifest the effectiveness of the Broad Learning System.