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In this work, we propose a novel convolutional autoencoder based architectureto generate subspace specific feature representations that are best suited forclassification task. The class-specific data is assumed to lie in lowdimensional linear subspaces, which could be noisy and not well separated,i.e., subspace distance (principal angle) between two classes is very low. Theproposed network uses a novel class-specific self expressiveness (CSSE) layersandwiched between encoder and decoder networks to generate class-wise subspacerepresentations which are well separated. The CSSE layer along with encoder/decoder are trained in such a way that data still lies in subspaces in thefeature space with minimum principal angle much higher than that of the inputspace. To demonstrate the effectiveness of the proposed approach, severalexperiments have been carried out on state-of-the-art machine learning datasetsand a significant improvement in classification performance is observed overexisting subspace based transformation learning methods.

Learning optimally separated class-specific subspace representations using convolutional autoencoder