Deep Low-Rank Subspace Clustering

Abstract

This paper is concerned with developing a novel approach to tackle the problem of subspace clustering. The approach introduces a convolutional autoencoder-based architecture to generate low-rank representations (LRR) of input data which are proven to be very suitable for subspace clustering. We propose to insert a fully-connected linear layer and its transpose between the encoder and decoder to implicitly impose a rank constraint on the learned representations. We train this architecture by minimizing a standard deep subspace clustering loss function and then recover underlying subspaces by applying a variant of spectral clustering technique. Extensive experiments on benchmark datasets demonstrate that the proposed model can not only achieve very competitive clustering results using a relatively small network architecture but also can maintain its high level of performance across a wide range of LRRs. This implies that the model can be appropriately combined with the state-of-the-art subspace clustering architectures to produce more accurate results.