Deep Latent Low-Rank Fusion Network for Progressive Subspace Discovery

Abstract

Low-rank representation is powerful for recover-ing and clustering the subspace structures, but it cannot obtain deep hierarchical information due to the single-layer mode. In this paper, we present a new and effective strategy to extend the sin-gle-layer latent low-rank models into multi-ple-layers, and propose a new and progressive Deep Latent Low-Rank Fusion Network (DLRF-Net) to uncover deep features and struc-tures embedded in input data. The basic idea of DLRF-Net is to refine features progressively from the previous layers by fusing the subspaces in each layer, which can potentially obtain accurate fea-tures and subspaces for representation. To learn deep information, DLRF-Net inputs shallow fea-tures of the last layers into subsequent layers. Then, it recovers the deeper features and hierar-chical information by congregating the projective subspaces and clustering subspaces respectively in each layer. Thus, one can learn hierarchical sub-spaces, remove noise and discover the underlying clean subspaces. Note that most existing latent low-rank coding models can be extended to multi-layers using DLRF-Net. Extensive results show that our network can deliver enhanced perfor-mance over other related frameworks.