gelnet.oneclass.obj: One-class regression objective function value

In gelnet: Generalized Elastic Nets

Description

Evaluates the one-class objective function value for a given model See details.

Usage

gelnet.oneclass.obj(w, X, l1, l2, d = rep(1, ncol(X)), P = diag(ncol(X)),
  m = rep(0, ncol(X)))

Arguments

w	p-by-1 vector of model weights
X	n-by-p matrix of n samples in p dimensions
l1	L1-norm penalty scaling factor λ_1
l2	L2-norm penalty scaling factor λ_2
d	p-by-1 vector of feature weights
P	p-by-p feature-feature penalty matrix
m	p-by-1 vector of translation coefficients

Details

Computes the objective function value according to

-\frac{1}{n} ∑_i s_i - \log( 1 + \exp(s_i) ) + R(w)

where

s_i = w^T x_i

R(w) = λ_1 ∑_j d_j |w_j| + \frac{λ_2}{2} (w-m)^T P (w-m)

Value

The objective function value.

See Also

gelnet

gelnet documentation built on May 2, 2019, 2:10 p.m.