{"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/learning-complex-valued-latent-filters-with","title":"Learning complex-valued latent filters with absolute cosine similarity","arxiv_id":null,"date":"2017-06-19","proceeding":null,"authors":["Anh H. T. Nguyen","V.G. Reju","Andy W. H. Khong","Ing Yann Soon"],"abstract":"We propose a new sparse coding technique based on the power mean of phase-invariant cosine distances. Our approach is a generalization of sparse filtering and K-hyperlines clustering. It offers a better sparsity enforcer than the L 1 /L 2 norm ratio that is typically used in sparse filtering. At the same time, the proposed approach scales better than the clustering counterparts for high-dimensional input. Our algorithm fully exploits the prior information obtained by preprocessing the observed data with whitening via an efficient row-wise decoupling scheme. In our simulating experiments, the algorithm produces better estimates than previous approaches do. It yields better separation of live recorded speech mixtures as well.","url_abs":"https://ieeexplore.ieee.org/document/7952589","url_pdf":"https://ieeexplore.ieee.org/document/7952589","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":"learning-complex-valued-latent-filters-with","repo_url":"https://github.com/karnwatcharasupat/directional-sparse-filtering-tf","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"learning-complex-valued-latent-filters-with","repo_url":"https://github.com/e13000/directional_sparse_filtering","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[{"method_slug":"directional-sparse-filtering","method_name":"Directional Sparse Filtering"}],"datasets_introduced":[],"methods_introduced":[{"slug":"directional-sparse-filtering","name":"Directional Sparse Filtering","full_name":"Directional Sparse FIltering"}],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}