  Most existing feature learning methods optimize inflexible handcrafted
features and the affinity matrix is constructed by shallow linear embedding
methods. Different from these conventional methods, we pretrain a generative
neural network by stacking convolutional autoencoders to learn the latent data
representation and then construct an affinity graph with them as a prior. Based
on the pretrained model and the constructed graph, we add a self-expressive
layer to complete the generative model and then fine-tune it with a new loss
function, including the reconstruction loss and a deliberately defined
locality-preserving loss. The locality-preserving loss designed by the
constructed affinity graph serves as prior to preserve the local structure
during the fine-tuning stage, which in turn improves the quality of feature
representation effectively. Furthermore, the self-expressive layer between the
encoder and decoder is based on the assumption that each latent feature is a
linear combination of other latent features, so the weighted combination
coefficients of the self-expressive layer are used to construct a new refined
affinity graph for representing the data structure. We conduct experiments on
four datasets to demonstrate the superiority of the representation ability of
our proposed model over the state-of-the-art methods.
