Regularization paTH for LASSO problem (thalasso)
thalasso solves problems of the following form:
minimize 1/2||X*beta-y||^2 + lambda*sum|beta_i|,
where X and y are problem data and beta and lambda are variables.
CALLING SEQUENCES [lambdas,betas,supports,alphas]=thalasso(X,y[,tolerance[,cond]])
INPUT X : NxP data matrix, N are the number of examples, P the number of features y : Nx1 data vector tolerance: scalar to indicate the last lambda accepted before 0 cond : scalar for matrix conditioning before inversion
OUTPUT lambdas : 1xM vector containing lambdas of the regularization path betas : PxM matrix containing optimize beta vector for each lambda supports : 1xM cell containing non-null beta indexes for each lambda alphas : PxM matrix containing the sub-differential of each component for each lambda
USAGE EXAMPLES [lambdas,betas]=thalasso(X,y); [lambdas,betas,supports,alphas]=thalasso(X,y,tolerance,cond)
- Changes to previous version:
Initial Announcement on mloss.org.
No one has posted any comments yet. Perhaps you'd like to be the first?
Leave a comment
You must be logged in to post comments.