{"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/subspace-co-clustering-with-two-way-graph","title":"Subspace Co-clustering with Two-Way Graph Convolution","arxiv_id":null,"date":"2022-02-01","proceeding":"CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management 2022 2","authors":["Chakib Fettal","Lazhar Labiod","Mohamed Nadif"],"abstract":"Subspace clustering aims to cluster high dimensional data lying in a union of low-dimensional subspaces. It has shown good results on the task of image clustering but text clustering, using documentterm matrices, proved more impervious to advances based on this approach. We hypothesize that this is because, compared to image data, text data is generally higher dimensional and sparser. This renders subspace clustering impractical in such a context. Here, we leverage subspace clustering for text by addressing these issues. We first extend the concept of subspace clustering to co-clustering, which has been extensively used on document-term matrices due to the resulting interplay between the document and term representations. We then address the sparsity problem through a two-way graph convolution, which promotes the grouping effect that has been credited for the effectiveness of some subspace clustering models. The proposed formulation results in an algorithm that is efficient both in terms of computational and spatial complexity. We show the competitiveness of our model w.r.t the state-of-the-art on document-term attributed graph datasets in terms of performance and efficiency.","url_abs":"https://dl.acm.org/doi/10.1145/3511808.3557706","url_pdf":"https://hal.science/hal-04467659v1/document","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":"subspace-co-clustering-with-two-way-graph","repo_url":"https://github.com/chakib401/SC3","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"image-clustering","task_name":"Image Clustering"},{"task_slug":"text-clustering","task_name":"Text Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}