df_md: Gradient of the Integrated Density on a Hyperplane

In DavidHofmeyr/PPCI: Projection Pursuit for Cluster Identification

Description

Finds the gradient of the integrated density of the best hyperplanes orthogonal to a given projection vector (assumes the data have zero mean vector). Used to obtain minimum density hyperplanes using gradient based optimisation.

Usage

df_md(v, X, P)

Arguments

v	a numeric vector of length ncol(X)
X	a numeric matrix (num_data x num_dimensions) to be projected on v
P	a list of parameters including (at least) $h (positive numeric bandwidth value), $alpha (positive numeric constraint width), $C (positive numeric affecting the slope of the penalty), $COV (covariance matrix of X)

Value

the (vector) gradient of the integrated density of the best hyperplane orthogonal to v.

DavidHofmeyr/PPCI documentation built on March 9, 2020, 5:05 p.m.