{ "_id": "6a141961acfb0bcc41d3cf09", "Package": "lolR", "Type": "Package", "Title": "Linear Optimal Low-Rank Projection", "Version": "2.1", "Date": "2020-06-20", "Authors@R": "c(person(\"Eric\", \"Bridgeford\", role = c(\"aut\", \"cre\"),\nemail = \"ericwb95@gmail.com\"),\nperson(\"Minh\", \"Tang\", role=\"ctb\",\nemail=\"minh@jhu.edu\"),\nperson(\"Jason\", \"Yim\", role = \"ctb\",\nemail = \"jasonkyuyim@gmail.com\"),\nperson(\"Joshua\", \"Vogelstein\", role = \"ths\",\nemail = \"jovo@jhu.edu\"))", "Maintainer": "Eric Bridgeford ", "Description": "Supervised learning techniques designed for the situation\nwhen the dimensionality exceeds the sample size have a tendency\nto overfit as the dimensionality of the data increases. To\nremedy this High dimensionality; low sample size (HDLSS)\nsituation, we attempt to learn a lower-dimensional\nrepresentation of the data before learning a classifier. That\nis, we project the data to a situation where the dimensionality\nis more manageable, and then are able to better apply standard\nclassification or clustering techniques since we will have\nfewer dimensions to overfit. A number of previous works have\nfocused on how to strategically reduce dimensionality in the\nunsupervised case, yet in the supervised HDLSS regime, few\nworks have attempted to devise dimensionality reduction\ntechniques that leverage the labels associated with the data.\nIn this package and the associated manuscript Vogelstein et al.\n(2017) , we provide several methods for\nfeature extraction, some utilizing labels and some not, along\nwith easily extensible utilities to simplify cross-validative\nefforts to identify the best feature extraction method.\nAdditionally, we include a series of adaptable benchmark\nsimulations to serve as a standard for future investigative\nefforts into supervised HDLSS. Finally, we produce a\ncomprehensive comparison of the included algorithms across a\nrange of benchmark simulations and real data applications.", "License": "GPL-2", "URL": "https://github.com/neurodata/lol", "Encoding": "UTF-8", "VignetteBuilder": "knitr", "RoxygenNote": "7.1.1", "Repository": "https://neurodata.r-universe.dev", "Date/Publication": "2021-02-23 06:15:00 UTC", "RemoteUrl": "https://github.com/neurodata/lol", "RemoteRef": "HEAD", "RemoteSha": "86698eae5c5ca4050c10e45ddcdfc24bc5c630f5", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-25 09:38:25 UTC", "User": "root" }, "Author": "Eric Bridgeford [aut, cre],\nMinh Tang [ctb],\nJason Yim [ctb],\nJoshua Vogelstein [ths]", "MD5sum": "e89a6147c9c6171eb739e4f72aaf9b32", "_user": "neurodata", "_type": "src", "_file": "lolR_2.1.tar.gz", "_fileid": "40952049bae102673d827ff540514c169a13fa1d62e106116419d62f2d2aa766", "_filesize": 7642028, "_sha256": "40952049bae102673d827ff540514c169a13fa1d62e106116419d62f2d2aa766", 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