Boosting sparsity-induced autoencoder: A novel sparse feature ensemble learning for image classification

Abstract

As a kind of unsupervised learning model, the autoencoder is usually adopted to perform the pretraining to obtain the optimal initial value of parameter space, so as to avoid the local minimality that the nonconvex problem may fall into and gradient vanishment of the process of back propagation. However, the autoencoder and its variants have not taken the statistical characteristics and domain knowledge of the train set and also lost plenty of essential representaions learned from different levels when it comes to image processing and computer vision. In this article, we firstly add a sparsity-induced layer into the autoencoder to exploit and extract more representative and essential features exist in the input and then combining the ensemble learning mechanism, we propose a novel sparse feature ensemble learning method, named Boosting sparsity-induced autoencoder, which could make full use of hierarchical and diverse features, increase the accuracy and the stability of a single model. The classification results on different data sets illustrated the effectiveness of our proposed method.