. . "Projection pursuit (Statistics)"@en . _:b7iddOtlocdOtgovauthoritiessubjectssh2018001844 . _:b7iddOtlocdOtgovauthoritiessubjectssh2018001844 _:b8iddOtlocdOtgovauthoritiessubjectssh2018001844 . _:b7iddOtlocdOtgovauthoritiessubjectssh2018001844 . _:b8iddOtlocdOtgovauthoritiessubjectssh2018001844 . _:b8iddOtlocdOtgovauthoritiessubjectssh2018001844 "Projection pursuit (Statistics)"@en . "150 $aProjection pursuit (Statistics)" . . . . . . . "sh2018001844" . . . _:b23iddOtlocdOtgovauthoritiessubjectssh2018001844 . _:b23iddOtlocdOtgovauthoritiessubjectssh2018001844 . _:b23iddOtlocdOtgovauthoritiessubjectssh2018001844 "Work cat: Uddin, M. Multivariate density estimation by projection pursuit methods, 1996:" . _:b23iddOtlocdOtgovauthoritiessubjectssh2018001844 "p.ii (The idea of using projections to investigate the properties of multivariate data has recently been exploited and named projection pursuit)"@en . _:b23iddOtlocdOtgovauthoritiessubjectssh2018001844 "found" . _:b31iddOtlocdOtgovauthoritiessubjectssh2018001844 . _:b31iddOtlocdOtgovauthoritiessubjectssh2018001844 . _:b31iddOtlocdOtgovauthoritiessubjectssh2018001844 "Jones, M.C. What is Projection Pursuit?, 1987:" . _:b31iddOtlocdOtgovauthoritiessubjectssh2018001844 "(Friedman and Tukey (1974) introduced the term \"projection pursuit\" for a technique for the exploratory analysis of multivariate data sets; the method seeks out \"interesting\" linear projections of the multivariate data onto a line or a plane)"@en . _:b31iddOtlocdOtgovauthoritiessubjectssh2018001844 "found" . _:b39iddOtlocdOtgovauthoritiessubjectssh2018001844 . _:b39iddOtlocdOtgovauthoritiessubjectssh2018001844 . _:b39iddOtlocdOtgovauthoritiessubjectssh2018001844 "Wikipedia, July 9, 2018" . _:b39iddOtlocdOtgovauthoritiessubjectssh2018001844 "(Projection pursuit (PP) is a type of statistical technique which involves finding the most \"interesting\" possible projections in multidimensional data. The idea of projection pursuit is to locate the projection or projections from high-dimensional space to low-dimensional space that reveal the most details about the structure of the data set. Once an interesting set of projections has been found, existing structures (clusters, surfaces, etc.) can be extracted and analyzed separately)"@en . _:b39iddOtlocdOtgovauthoritiessubjectssh2018001844 "found" . _:b47iddOtlocdOtgovauthoritiessubjectssh2018001844 . _:b47iddOtlocdOtgovauthoritiessubjectssh2018001844 . _:b47iddOtlocdOtgovauthoritiessubjectssh2018001844 "2018-07-09T00:00:00"^^ . _:b47iddOtlocdOtgovauthoritiessubjectssh2018001844 "new"^^ . _:b47iddOtlocdOtgovauthoritiessubjectssh2018001844 . _:b47iddOtlocdOtgovauthoritiessubjectssh2018001844 . _:b55iddOtlocdOtgovauthoritiessubjectssh2018001844 . _:b55iddOtlocdOtgovauthoritiessubjectssh2018001844 . _:b55iddOtlocdOtgovauthoritiessubjectssh2018001844 "2018-10-04T14:37:57"^^ . _:b55iddOtlocdOtgovauthoritiessubjectssh2018001844 "revised"^^ . _:b55iddOtlocdOtgovauthoritiessubjectssh2018001844 . _:b55iddOtlocdOtgovauthoritiessubjectssh2018001844 .