DMRAE: discriminative manifold regularized auto-encoder for sparse and robust feature learning

Although the regularized over-complete auto-encoders have shown great ability to extract meaningful representation from data and reveal the underlying manifold of them, their unsupervised learning nature prevents the consideration of class distinction in the representations. The present study aimed to learn sparse, robust, and discriminative features through supervised manifold regularized auto-encoders by preserving locality on the manifold directions around each data and enhancing between-class discrimination. The combination of triplet loss manifold regularization with a novel denoising regularizer is injected to the objective function to generate features which are robust against perpendicular perturbation around data manifold and are sensitive enough to variation along the manifold. Also, the sparsity ratio of the obtained representation is adaptive based on the data distribution. The experimental results on 12 real-world classification problems show that the proposed method has better classification performance in comparison with several recently proposed relevant models.