{ "_id": "6a17da29acfb0bcc41d9b6bc", "Package": "GeneralizedUmatrixGPU", "Type": "Package", "Title": "Credible Visualization for Two-Dimensional Projections of Data", "Version": "0.1.14", "Date": "2026-01-26", "Authors@R": "c(person(\"Quirin\", \"Stier\", email= \"Quirin_Stier@gmx.de\",role=c(\"aut\",\"cre\"), comment = c(ORCID = \"0000-0002-7896-4737\")), person(\"Michael\", \"Thrun\", role=c(\"aut\",\"cph\"), comment = c(ORCID = \"0000-0001-9542-5543\")), person(\"The Khronos Group Inc.\", role=c(\"cph\")))", "Maintainer": "Quirin Stier ", "Description": "Projections are common dimensionality reduction methods,\nwhich represent high-dimensional data in a two-dimensional\nspace. However, when restricting the output space to two\ndimensions, which results in a two dimensional scatter plot\n(projection) of the data, low dimensional similarities do not\nrepresent high dimensional distances coercively [Thrun, 2018]\n. This could lead to a\nmisleading interpretation of the underlying structures [Thrun,\n2018]. By means of the 3D topographic map the generalized\nUmatrix is able to depict errors of these two-dimensional\nscatter plots. The package is derived from the book of Thrun,\nM.C.: \"Projection Based Clustering through Self-Organization\nand Swarm Intelligence\" (2018) \nand the main algorithm called simplified self-organizing map\nfor dimensionality reduction methods is published in Thrun,\nM.C. and Ultsch, A.: \"Uncovering High-dimensional Structures of\nProjections from Dimensionality Reduction Methods\" (2020)\n.", "License": "GPL-3", "NeedsCompilation": "yes", "SystemRequirements": "C++17, GNU make, pandoc (>=1.12.3, needed for\nvignettes), OpenCL shared library (provided by an SDK such as\nAMD/NVIDIA)", "LazyLoad": "yes", "LazyData": "TRUE", "Encoding": "UTF-8", "Packaged": { "Date": "2026-05-28 05:57:48 UTC", "User": "root" }, "Author": "Quirin Stier [aut, cre] (ORCID:\n), Michael Thrun [aut,\ncph] (ORCID: ), The\nKhronos Group Inc. 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