{"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/active-semi-supervised-learning-using","title":"Active Semi-Supervised Learning Using Sampling Theory for Graph Signals","arxiv_id":"1405.4324","date":"2014-05-16","proceeding":null,"authors":["Akshay Gadde","Aamir Anis","Antonio Ortega"],"abstract":"We consider the problem of offline, pool-based active semi-supervised\nlearning on graphs. This problem is important when the labeled data is scarce\nand expensive whereas unlabeled data is easily available. The data points are\nrepresented by the vertices of an undirected graph with the similarity between\nthem captured by the edge weights. Given a target number of nodes to label, the\ngoal is to choose those nodes that are most informative and then predict the\nunknown labels. We propose a novel framework for this problem based on our\nrecent results on sampling theory for graph signals. A graph signal is a\nreal-valued function defined on each node of the graph. A notion of frequency\nfor such signals can be defined using the spectrum of the graph Laplacian\nmatrix. The sampling theory for graph signals aims to extend the traditional\nNyquist-Shannon sampling theory by allowing us to identify the class of graph\nsignals that can be reconstructed from their values on a subset of vertices.\nThis approach allows us to define a criterion for active learning based on\nsampling set selection which aims at maximizing the frequency of the signals\nthat can be reconstructed from their samples on the set. Experiments show the\neffectiveness of our method.","url_abs":"http://arxiv.org/abs/1405.4324v1","url_pdf":"http://arxiv.org/pdf/1405.4324v1.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":"active-semi-supervised-learning-using","repo_url":"https://github.com/broshanfekr/Active-Semi-Supervised-Learning-Using-Sampling-Theory-for-Graph-signals","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}