Broad learning system: Feature extraction based on K-means clustering algorithm

Broad Learning System [1] proposed recently demonstrates efficient and effective learning capability. This model is also proved to be suitable for incremental learning algorithms by taking the advantages of random vector flat neural networks. In this paper, a modified BLS structure based on the K-means feature extraction is developed. Compared with the original broad learning system, acceptable performance on more complicated data set, such as CIFAR-10, is achieved. Furthermore, it is proved that the proposed model in [1] is flexible and potential in various applications.