
 Description:
Regularization paTH for LASSO problem (thalasso)
thalasso solves problems of the following form:
minimize 1/2X*betay^2 + lambda*sumbeta_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 nonnull beta indexes for each lambda alphas : PxM matrix containing the subdifferential 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.
 BibTeX Entry: Download
 URL: Project Homepage
 Supported Operating Systems: Agnostic
 Data Formats: Matlab
 Tags: Lars, Lasso
 Archive: download here
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