  Deep learning is a kind of feature learning method with strong nonliear
feature transformation and becomes more and more important in many fields of
artificial intelligence. Deep autoencoder is one representative method of the
deep learning methods, and can effectively extract abstract the information of
datasets. However, it does not consider the complementarity between the deep
features and original features during deep feature transformation. Besides, it
suffers from small sample problem. In order to solve these problems, a novel
deep autoencoder - hybrid feature embedded stacked sparse autoencoder(HESSAE)
has been proposed in this paper. HFESAE is capable to learn discriminant deep
features with the help of embedding original features to filter weak
hidden-layer outputs during training. For the issue that class representation
ability of abstract information is limited by small sample problem, a feature
fusion strategy has been designed aiming to combining abstract information
learned by HFESAE with original feature and obtain hybrid features for feature
reduction. The strategy is hybrid feature selection strategy based on L1
regularization followed by an support vector machine(SVM) ensemble model, in
which weighted local discriminant preservation projection (w_LPPD), is designed
and employed on each base classifier. At the end of this paper, several
representative public datasets are used to verify the effectiveness of the
proposed algorithm. The experimental results demonstrated that, the proposed
feature learning method yields superior performance compared to other existing
and state of art feature learning algorithms including some representative deep
autoencoder methods.
