gelnet.oneclass.obj {gelnet}

One-class regression objective function value

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 \lambda_1

l2

L2-norm penalty scaling factor \lambda_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} \sum_i s_i - \log( 1 + \exp(s_i) ) + R(w)

where

s_i = w^T x_i

R(w) = \lambda_1 \sum_j d_j |w_j| + \frac{\lambda_2}{2} (w-m)^T P (w-m)

Value

The objective function value.

See Also

gelnet