Description Usage Arguments Details Value Author(s) References Examples
Solve the generalized DWD model by using a symmetric Gauss-Seidel based alternating direction method of multipliers (ADMM) method.
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X |
A d x n matrix of n training samples with d features. |
y |
A vector of length n of training labels. The element of |
C |
A number representing the penalty parameter for the generalized DWD model. |
expon |
A positive number representing the exponent q of the residual r_i in the generalized DWD model. Common choices are |
tol |
The stopping tolerance for the algorithm. (Default = 1e-5) |
maxIter |
Maximum iteration allowed for the algorithm. (Default = 2000) |
method |
Method for solving generalized DWD model. The default is set to be 1 for the highly efficient sGS-ADMM algorithm. User can also select |
printDetails |
Switch for printing details of the algorithm. Default is set to be 0 (not printing). |
rmzeroFea |
Switch for removing zero features in the data matrix. Default is set to be 1 (removing zero features). |
scaleFea |
Switch for scaling features in the data matrix. This is to make the features having roughly similar magnitude. Default is set to be 1 (scaling features). |
This is a symmetric Gauss-Seidel based alternating method of multipliers (sGS-ADMM) algorithm for solving the generalized DWD model of the following formulation:
\min ∑_i θ_q (r_i) + C e^T x_i
subject to the constraints
Z^T w + β y + ξ - r = 0, ||w||<=1, ξ>=0,
where Z = X diag(y), e is a given positive vector such that ||e||_∞ = 1, and θ_q
is a function defined by θ_q(t) = 1/t^q if t>0 and θ_q(t)=∞ if t<=0.
A list consists of the result from the algorithm.
w |
The unit normal of hyperplane that distinguishes the two classes. |
beta |
The distance of the hyperplane to the origin (β in the above formulation). |
xi |
A slack variable of length n for the possibility that the two classes may not be separated cleanly by the hyperplane (ξ in the above formulation). |
r |
The residual r:= Z^T w + β y + ξ. |
alpha |
Dual variable of the generalized DWD model. |
info |
A list consists of the information from the algorithm. |
runhist |
A list consists of the run history throughout the iterations. |
Xin-Yee Lam, J.S. Marron, Defeng Sun, and Kim-Chuan Toh
Lam, X.Y., Marron, J.S., Sun, D.F., and Toh, K.C. (2018)
“Fast algorithms for large scale generalized distance weighted discrimination", Journal of Computational and Graphical Statistics, forthcoming.
https://arxiv.org/abs/1604.05473
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