We present a novel deep neural network architecture for unsupervised subspace clustering. This architecture is built upon deep auto-encoders, which non-linearly map the input data into a latent space. Our key idea is to introduce a novel self-expressive layer between the encoder and the decoder to mimic the "selfexpressiveness" property that has proven effective in traditional subspace clustering. Being differentiable, our new self-expressive layer provides a simple but effective way to learn pairwise affinities between all data points through a standard backpropagation procedure. Being nonlinear, our neural-network based method is able to cluster data points having complex (often nonlinear) structures. We further propose pre-training and fine-tuning strategies that let us effectively learn the parameters of our subspace clustering networks. Our experiments show that the proposed method significantly outperforms the state-of-the-art unsupervised subspace clustering methods.
Instead of using hand-crafted features, Deep subspace clustering Networks (DSC-NET) REF employ the deep convolutional Auto-encoder to nonlinearly map the images to a latent space, and make use of a self-expressive layer between the encoder and the decoder to learn the affinities between all the data points.
Deep Subspace Clustering Networks
ArXiv
ArXiv
Computer Science