gelnet (version 1.2.1)

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 $$s_i=w^T{x}_i$$ $$R(w)={\lambda }_1\sum _j{d}_j|w_j|+\frac{{\lambda }_2}{2}(w-m{)}^{T}P(w-m)$$

See Also

gelnet