Class-specific pre-trained sparse autoencoders for learning effective features for document classification

Sparse autoencoder is a commonly used deep learning approach for automatically learning features from unlabelled data (unsupervised feature learning). This paper proposes class-specific (supervised) pre-trained approach based on sparse autoencoder to gain low-dimensional interesting structure of features with high performance in document classification. Experimental results have demonstrated the advantages and usefulness of the proposed method in document classification in high-dimensional feature space, in terms of the limited number of features required to achieve good classification accuracy.