Autoencoder and Hypergraph-Based Semi-Supervised Broad Learning System

The linear sparse feature extraction method used in the classical broad learning system(BLS) is difficult to extract the complex nonlinear features of data effectively. In addition, when the number of labeled samples is small, the generalization ability of BLS cannot be guaranteed. To solve these problems, a novel autoencoder and hypergraph-based semi-supervised BLS(AH-SBLS) is proposed. The main steps of AH-SBLS are described as follows. Firstly, we use all labeled and unlabeled samples to train the autoencoder, and then the trained autoencoder is used to extract the features of input data automatically. Secondly, the extracted features are viewed as the feature nodes of AH-SBLS and are further broadened. In the third step, a semi-supervised hypergraph is constructed to express the high-order manifold relationship between labeled and unlabeled samples, and the hypergraph regularization term is introduced into the objective function of AH-SBLS. Finally, the objective function of AH-SBLS is solved by ridge regression and thus the labels of unlabeled samples can be predicted. Experimental results of image classification show that AH-SBLS can achieve higher classification accuracy in semi-supervised classification tasks. © 2022, Chinese Institute of Electronics. All right reserved.