gelnet.oneclass.obj: 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
Value
The objective function value.
Details
Computes the objective function value according to
$$-\frac{1}{n} \sum_i s_i - \log( 1 + \exp(s_i) ) + R(w)$$
where
$$s_i = w^T x_i$$
$$R(w) = \lambda_1 \sum_j d_j |w_j| + \frac{\lambda_2}{2} (w-m)^T P (w-m)$$