Broad Learning Autoencoder With Graph Structure for Data Clustering

Broad learning system (BLS) is a simple yet efficient learning algorithm that only needs to train a three-layer feedforward neural network. Although various BLS variants have been designed for supervised learning, none have been used for unsupervised learning. This paper proposes BLS-AE, a novel data clustering scheme that seamlessly combines BLS and auto-encoder. Then, graph regularization is introduced into BLS-AE to increase the capability of learning intrinsic structures in data and adaptation to various data simultaneously, which is termed BLSg-AE. Moreover, different concatenation styles of feature and enhancement nodes are investigated for reusing the learned features, followed by designing two special strategies (i.e., pruning optimization and incremental learning) to reduce the parameter scale significantly and improve performance, which is termed xBLSg-AE. To address the performance instability issue caused by random subspace in a single xBLSg-AE, the x-cascade broad learning system graph regularization multi-auto-encoder (xBLSg-MAE) algorithm is proposed. Extensive experiments are conducted on multiple real data sets to demonstrate that the proposed methods are more effective and robust than competing approaches.