Deep subspace clustering via latent representation learning

Abstract

Deep subspace clustering networks (DSC-Nets), which combine deep autoencoders and self-expressive modules, have garnered widespread attention due to their outstanding performance. Within these networks, the autoencoder captures the latent representations of data by reconstructing the input data, while the self-expressive layer learns an affinity matrix based on these latent representations. This matrix guides spectral clustering, ultimately completing the clustering task. However, the latent representations learned solely through self-reconstruction by the autoencoder lack discriminative power. The quality of these latent representations directly affects the performance of the affinity matrix, which inevitably limits the clustering performance. To address this issue, we propose learning dissimilar relationships between samples using a classification module, and similar relationships using the self-expressive module. We integrate the information from both modules to construct a graph based on learned similarities, which is then embedded into the autoencoder network. Furthermore, we introduce a pseudo-label supervision module to guide the learning of higher-level similarities in the latent representations, thus achieving more discriminative latent features. Additionally, to enhance the quality of the affinity matrix, we employ an entropy norm constraint to improve connectivity within the subspaces. Experimental results on four public datasets demonstrate that our method achieves superior performance compared to other popular subspace clustering approaches.