gelnet_oclr_opt: GELnet optimizer for one-class logistic regression

In ArtemSokolov/gelnet: Generalized Elastic Nets

Description

Constructs a GELnet model for one-class regression using the Newton method.

Usage

gelnet_oclr_opt(X, l1, l2, max_iter = 100L, eps = 1e-05,
  silent = FALSE, verbose = FALSE, nonneg = FALSE, w_init = NULL,
  d = NULL, P = NULL, m = NULL)

Arguments

X	n-by-p matrix of n samples in p dimensions
l1	coefficient for the L1-norm penalty
l2	coefficient for the L2-norm penalty
max_iter	maximum number of iterations
eps	convergence precision
silent	set to TRUE to suppress run-time output to stdout (default: FALSE)
nonneg	set to TRUE to enforce non-negativity constraints on the weights (default: FALSE )
w_init	initial parameter estimate for the weights
d	p-by-1 vector of feature weights
P	p-by-p feature association penalty matrix
m	p-by-1 vector of translation coefficients

Details

The function optimizes the following objective:

-\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)

The method operates by constructing iteratively re-weighted least squares approximations of the log-likelihood loss function and then calling the linear regression routine to solve those approximations. The least squares approximations are obtained via the Taylor series expansion about the current parameter estimates.

Value

A list with one element:

w

p-by-1 vector of p model weights

ArtemSokolov/gelnet documentation built on Sept. 13, 2019, 4:01 a.m.