SDAP: Sparse Discriminant Analysis solved via Proximal Gradient

In accSDA: Accelerated Sparse Discriminant Analysis

Sparse Discriminant Analysis solved via Proximal Gradient

Description

Applies proximal gradient algorithm to the optimal scoring formulation of sparse discriminant analysis proposed by Clemmensen et al. 2011.

Usage

SDAP(x, ...)

## Default S3 method:
SDAP(
  Xt,
  Yt,
  Om,
  gam,
  lam,
  q,
  PGsteps,
  PGtol,
  maxits,
  tol,
  initTheta,
  bt = FALSE,
  L,
  eta
)

Arguments

Xt	n by p data matrix, (not a data frame, but a matrix)
Yt	n by K matrix of indicator variables (Yij = 1 if i in class j). This will later be changed to handle factor variables as well. Each observation belongs in a single class, so for a given row/observation, only one element is 1 and the rest is 0.
Om	p by p parameter matrix Omega in generalized elastic net penalty.
gam	Regularization parameter for elastic net penalty.
lam	Regularization parameter for l1 penalty, must be greater than zero.
q	Desired number of discriminant vectors.
PGsteps	Maximum number if inner proximal gradient algorithm for finding beta.
PGtol	Stopping tolerance for inner APG method.
maxits	Number of iterations to run
tol	Stopping tolerance for proximal gradient algorithm.
initTheta	Initial first theta, default value is a vector of ones.
bt	Boolean to indicate whether backtracking should be used, default false.
L	Initial estimate for Lipshitz constant used for backtracking.
eta	Scalar for Lipshitz constant.

Value

SDAP returns an object of class "SDAP" including a list with the following named components: (More will be added later to handle the predict function)

call

The matched call.

B

p by q matrix of discriminant vectors.

Q

K by q matrix of scoring vectors.

subits

Total number of iterations in proximal gradient subroutine.

totalits

Number coordinate descent iterations for all discriminant vectors

NULL

See Also

SDAPcv, SDAAP and SDAD

accSDA documentation built on Sept. 5, 2022, 5:05 p.m.