A CNN-Based Broad Learning System
Fan Yang · 2018
Deep learning is a very active research field in machine learning. The most popular used convolutional neural networks (CNN) is one of deep learning models. However., deep learning suffers from a time consuming training process because of a large number of parameters in filters and layers to be learned. Broad Learning System (BLS) offers an alternative way of machine learning in deep structure. BLS is established based on the idea of the random vector function-link neural network (RVFLNN) which eliminates the drawback of long training process and also provides the generalization capability in function approximation. In this paper, a CNN-based broad learning system (CNNBLS) which is more useful than BLS for computer vision is proposed. In the proposed method, convolution and max pooling are used for feature extraction and then the principal component analysis (PCA) is used for feature dimensionality reduction, following a ridge regression learning for pattern classification. Parameters in convolution filters are randomly initialized which is similar with BLS and RVFLNN. Therefore the proposed method needs less parameters to be optimized at the same time it adopts the very useful feature extraction method for computer vision. Experimental results on the MNIST handwritten digits recognition data set and the NYU NORB object recognition data set demonstrate the effectiveness of the proposed method.