基于自编码器和超图的半监督宽度学习系统

Autoencoder and Hypergraph-Based Semi-Supervised Broad Learning System

常规宽度学习系统(Broad Learning System,BLS)通常采用的线性稀疏特征提取方法难以对数据的复杂非线性特征进行有效表征.此外,当标记样本量较少时,BLS的泛化性能难以得到保证.为此,提出一种基于自编码器和超图的半监督宽度学习系统(Autoencoder and Hypergraph-based Semi-supervised BLS,AH-SBLS).主要步骤为:首先,使用包括标记样本和无标记样本在内的全部样本训练自编码器,利用训练好的自编码器自动提取数据的复杂非线性特征;其次,将自编码器特征层中的特征作为AH-SBLS的特征节点并对其进行宽度拓展;然后,构造半监督超图以挖掘标记样本和无标记样本间的高阶流形关系,并将超图正则项引入宽度学习系统的目标函数中;最后,利用岭回归对目标函数进行求解,实现对无标记样本的类别预测.在图像分类实验上的结果表明,AH-SBLS能够实现半监督分类且获得较高的分类精度.

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 gen⁃eralization ability of BLS cannot be guaranteed.To solve these problems,a novel autoencoder and hypergraph-based semisupervised 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.Final⁃ly,the objective function of AH-SBLS is solved by ridge regression and thus the labels of unlabeled samples can be predict⁃ed.Experimental results of image classification show that AH-SBLS can achieve higher classification accuracy in semi-su⁃pervised classification tasks.