Progressive Deep Non-Negative Matrix Factorization Architecture with Graph Convolution-based Basis Image Reorganization

Highlights：

• An idea to describe the expressiveness of basis images and a method for semantic reconstruction of basis images are proposed based on graph convolution. The basis image reconstruction based on this idea will not severely destroy the expressive ability of each basis image. Meanwhile, the robustness of the basis image is enhanced to support its deeper factorization.

• The basis image reconstructed based on proposed attribute similarity graph (ASG) can withstand deeper factorization.

• Deeper factorization result can be obtained by PDNMF than existing NMF-based methods.

• The recognition accuracy of PDNMF shows an overall upward trend with the increase of the number of layers.

摘要

•A novel progressive deep non-negative matrix factorization (PDNMF) architecture is proposed. Different from the existing deep NMF-based methods that continuously factorizes the basis images, the PDNMF progressively decomposes the basis images in a cyclic form of “factorization-reconstruction-factorization”.•An idea to describe the expressiveness of basis images and a method for semantic reconstruction of basis images are proposed based on graph convolution. The basis image reconstruction based on this idea will not severely destroy the expressive ability of each basis image. Meanwhile, the robustness of the basis image is enhanced to support its deeper factorization.•The basis image reconstructed based on proposed attribute similarity graph (ASG) can withstand deeper factorization.•Deeper factorization result can be obtained by PDNMF than existing NMF-based methods.•The recognition accuracy of PDNMF shows an overall upward trend with the increase of the number of layers.