Discriminative Graph Regularized Broad Learning System for Image Recognition


Broad Learning System (BLS) has been proposed as an alternative method of deep learning. It has various advantages over traditional layered neural networks. The first one lies in the fact the input is randomly mapped into series of feature spaces and the output weights can be determined analytically. The second one is that the network structure is expanded broadly by the so-called
enhancement nodes”. And the third characteristic is the BLS can be learned incrementally without a retraining process when there comes new input data or neural nodes. It has been proven that BLS can overcome the inadequacies caused by training a large number of parameters in gradient-based deep learning algorithms. In this paper, a novel variant Graph Regularized Broad Learning System (GBLS) is proposed. Taking account of the locally invariant property of data, which means the similar images may share similar properties, the manifold learning is incorporated into the objective function of the standard BLS. In GBLS, the output weights are constrained to learn more discriminative information, and the classification ability can be further enhanced. Several experiments are carried out to verify that our proposed GBLS model can outperform the standard BLS. What is more, the GBLS also performs better compared with other state-of-the-art image recognition methods.



Junwei Jin received the bachelor’s degree from the Ningxia University, Ningxia, China in 2013 and the M.S. degree from the University of Macau, Macau, China in 2015, where he is currently pursuing the Ph.D degree with the Faculty of Science and Technology. His current research interests include machine learning, deep and broad learning, and sparse representation.