PPCI - Projection Pursuit for Cluster Identification
Implements recently developed projection pursuit algorithms for finding optimal linear cluster separators. The clustering algorithms use optimal hyperplane separators based on minimum density, Pavlidis et. al (2016) <https://jmlr.csail.mit.edu/papers/volume17/15-307/15-307.pdf>; minimum normalised cut, Hofmeyr (2017) <doi:10.1109/TPAMI.2016.2609929>; and maximum variance ratio clusterability, Hofmeyr and Pavlidis (2015) <doi:10.1109/SSCI.2015.116>.
Last updated 5 years ago
3.26 score 2 stars 18 scripts 173 downloadsFKSUM - Fast Kernel Sums
Implements the method of Hofmeyr, D.P. (2021) <DOI:10.1109/TPAMI.2019.2930501> for fast evaluation of univariate kernel smoothers based on recursive computations. Applications to the basic problems of density and regression function estimation are provided, as well as some projection pursuit methods for which the objective is based on non-parametric functionals of the projected density, or conditional density of a response given projected covariates. The package is accompanied by an instructive paper in the Journal of Statistical Software <doi:10.18637/jss.v101.i03>.
Last updated 2 years ago
1.48 score 1 packages 2 scripts 304 downloads