Skip navigation
Skip navigation

Deep subspace clustering networks

Ji, Pan; Zhang, Tong; Li, Hongdong; Salzmann, Mathieu; Reid, Ian


We present a novel deep neural network architecture for unsupervised subspace clustering. This architecture is built upon deep auto-encoders, which non-linearly map the input data into a latent space. Our key idea is to introduce a novel self-expressive layer between the encoder and the decoder to mimic the "self-expressiveness" property that has proven effective in traditional subspace clustering. Being differentiable, our new self-expressive layer provides a simple but effective way to learn...[Show more]

CollectionsANU Research Publications
Date published: 2017
Type: Conference paper
Source: Proceedings of the 31st Annual Conference on Neural Information Processing Systems, NIPS 2017
DOI: 978-1-5108-6096-4
Access Rights: Open Access


File Description SizeFormat Image
01_Ji_Deep_subspace_clustering_2017.pdf1.81 MBAdobe PDFThumbnail

Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  17 November 2022/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator