{"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/clustering-noisy-signals-with-structured","title":"Clustering Noisy Signals with Structured Sparsity Using Time-Frequency Representation","arxiv_id":"1510.05214","date":"2015-10-18","proceeding":null,"authors":["Tom Hope","Avishai Wagner","Or Zuk"],"abstract":"We propose a simple and efficient time-series clustering framework\nparticularly suited for low Signal-to-Noise Ratio (SNR), by simultaneous\nsmoothing and dimensionality reduction aimed at preserving clustering\ninformation. We extend the sparse K-means algorithm by incorporating structured\nsparsity, and use it to exploit the multi-scale property of wavelets and group\nstructure in multivariate signals. Finally, we extract features invariant to\ntranslation and scaling with the scattering transform, which corresponds to a\nconvolutional network with filters given by a wavelet operator, and use the\nnetwork's structure in sparse clustering. By promoting sparsity, this transform\ncan yield a low-dimensional representation of signals that gives improved\nclustering results on several real datasets.","url_abs":"http://arxiv.org/abs/1510.05214v1","url_pdf":"http://arxiv.org/pdf/1510.05214v1.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":"clustering-noisy-signals-with-structured","repo_url":"https://github.com/avishaiwa/SPARCWave","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-clustering","task_name":"Time Series Clustering"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}