{"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/iterative-gaussianization-from-ica-to-random","title":"Iterative Gaussianization: from ICA to Random Rotations","arxiv_id":"1602.00229","date":"2016-01-31","proceeding":"IEEE Transactions on Neural Networks 2011 2","authors":["Valero Laparra","Gustavo Camps-Valls","Jesús Malo"],"abstract":"Most signal processing problems involve the challenging task of\nmultidimensional probability density function (PDF) estimation. In this work,\nwe propose a solution to this problem by using a family of Rotation-based\nIterative Gaussianization (RBIG) transforms. The general framework consists of\nthe sequential application of a univariate marginal Gaussianization transform\nfollowed by an orthonormal transform. The proposed procedure looks for\ndifferentiable transforms to a known PDF so that the unknown PDF can be\nestimated at any point of the original domain. In particular, we aim at a zero\nmean unit covariance Gaussian for convenience. RBIG is formally similar to\nclassical iterative Projection Pursuit (PP) algorithms. However, we show that,\nunlike in PP methods, the particular class of rotations used has no special\nqualitative relevance in this context, since looking for interestingness is not\na critical issue for PDF estimation. The key difference is that our approach\nfocuses on the univariate part (marginal Gaussianization) of the problem rather\nthan on the multivariate part (rotation). This difference implies that one may\nselect the most convenient rotation suited to each practical application. The\ndifferentiability, invertibility and convergence of RBIG are theoretically and\nexperimentally analyzed. Relation to other methods, such as Radial\nGaussianization (RG), one-class support vector domain description (SVDD), and\ndeep neural networks (DNN) is also pointed out. The practical performance of\nRBIG is successfully illustrated in a number of multidimensional problems such\nas image synthesis, classification, denoising, and multi-information\nestimation.","url_abs":"http://arxiv.org/abs/1602.00229v1","url_pdf":"http://arxiv.org/pdf/1602.00229v1.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":"iterative-gaussianization-from-ica-to-random","repo_url":"https://github.com/IPL-UV/rbig_jax","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"jax","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"iterative-gaussianization-from-ica-to-random","repo_url":"https://github.com/IPL-UV/gauss4eo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"iterative-gaussianization-from-ica-to-random","repo_url":"https://github.com/IPL-UV/rbig","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"iterative-gaussianization-from-ica-to-random","repo_url":"https://github.com/IPL-UV/rbig_matlab","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"iterative-gaussianization-from-ica-to-random","repo_url":"https://github.com/jejjohnson/rbig","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"iterative-gaussianization-from-ica-to-random","repo_url":"https://github.com/lucastheis/isa","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"iterative-gaussianization-from-ica-to-random","repo_url":"https://github.com/spencerkent/pyRBIG","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"image-generation","task_name":"Image Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1602.00229","atlas_url":"https://app.syntology.ai/?focus=1602.00229","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1602.00229"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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