{"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/whitening-free-least-squares-non-gaussian","title":"Whitening-Free Least-Squares Non-Gaussian Component Analysis","arxiv_id":"1603.01029","date":"2016-03-03","proceeding":null,"authors":["Hiroaki Shiino","Hiroaki Sasaki","Gang Niu","Masashi Sugiyama"],"abstract":"Non-Gaussian component analysis (NGCA) is an unsupervised linear dimension\nreduction method that extracts low-dimensional non-Gaussian \"signals\" from\nhigh-dimensional data contaminated with Gaussian noise. NGCA can be regarded as\na generalization of projection pursuit (PP) and independent component analysis\n(ICA) to multi-dimensional and dependent non-Gaussian components. Indeed,\nseminal approaches to NGCA are based on PP and ICA. Recently, a novel NGCA\napproach called least-squares NGCA (LSNGCA) has been developed, which gives a\nsolution analytically through least-squares estimation of log-density gradients\nand eigendecomposition. However, since pre-whitening of data is involved in\nLSNGCA, it performs unreliably when the data covariance matrix is\nill-conditioned, which is often the case in high-dimensional data analysis. In\nthis paper, we propose a whitening-free LSNGCA method and experimentally\ndemonstrate its superiority.","url_abs":"http://arxiv.org/abs/1603.01029v2","url_pdf":"http://arxiv.org/pdf/1603.01029v2.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":"whitening-free-least-squares-non-gaussian","repo_url":"https://github.com/hgeno/WFLSNGCA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"}],"methods":[{"method_slug":"ica","method_name":"ICA"}],"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}