{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-low-rank-subspace-clustering","title":"Deep Low-Rank Subspace Clustering","arxiv_id":null,"date":"2020-06-18","proceeding":null,"authors":["Mohsen Kheirandishfard","Fariba Zohrizadeh","Farhad Kamangar"],"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.","url_abs":"https://ieeexplore.ieee.org/abstract/document/9150993","url_pdf":"https://openaccess.thecvf.com/content_CVPRW_2020/papers/w50/Kheirandishfard_Deep_Low-Rank_Subspace_Clustering_CVPRW_2020_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-low-rank-subspace-clustering","repo_url":"https://github.com/mohsenkheirandishfard/DLRSC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"decoder","task_name":"Decoder"}],"methods":[{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"spectral-clustering","method_name":"Spectral Clustering"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}