{"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/block-model-guided-unsupervised-feature","title":"Block Model Guided Unsupervised Feature Selection","arxiv_id":"2007.02376","date":"2020-07-05","proceeding":null,"authors":["Zilong Bai","Hoa Nguyen","Ian Davidson"],"abstract":"Feature selection is a core area of data mining with a recent innovation of graph-driven unsupervised feature selection for linked data. In this setting we have a dataset $\\mathbf{Y}$ consisting of $n$ instances each with $m$ features and a corresponding $n$ node graph (whose adjacency matrix is $\\mathbf{A}$) with an edge indicating that the two instances are similar. Existing efforts for unsupervised feature selection on attributed networks have explored either directly regenerating the links by solving for $f$ such that $f(\\mathbf{y}_i,\\mathbf{y}_j) \\approx \\mathbf{A}_{i,j}$ or finding community structure in $\\mathbf{A}$ and using the features in $\\mathbf{Y}$ to predict these communities. However, graph-driven unsupervised feature selection remains an understudied area with respect to exploring more complex guidance. Here we take the novel approach of first building a block model on the graph and then using the block model for feature selection. That is, we discover $\\mathbf{F}\\mathbf{M}\\mathbf{F}^T \\approx \\mathbf{A}$ and then find a subset of features $\\mathcal{S}$ that induces another graph to preserve both $\\mathbf{F}$ and $\\mathbf{M}$. We call our approach Block Model Guided Unsupervised Feature Selection (BMGUFS). Experimental results show that our method outperforms the state of the art on several real-world public datasets in finding high-quality features for clustering.","url_abs":"https://arxiv.org/abs/2007.02376v1","url_pdf":"https://arxiv.org/pdf/2007.02376v1.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":"block-model-guided-unsupervised-feature","repo_url":"https://github.com/ZilongBai/KDD2020BMGUFS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"block-model-guided-unsupervised-feature","repo_url":"https://github.com/MindSpore-scientific-2/code-4/tree/main/Block_Model","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"block-model-guided-unsupervised-feature","repo_url":"https://github.com/MindSpore-scientific-2/code-5/tree/main/Block_Model","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"block-model-guided-unsupervised-feature","repo_url":"https://github.com/pwc-1/Paper-9/tree/main/4/Block_Model","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"feature-selection","task_name":"feature selection"},{"task_slug":"model","task_name":"model"}],"methods":[{"method_slug":"feature-selection","method_name":"Feature Selection"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}