Description:

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.

BibTeX Entry: Download

Supported Operating Systems: Agnostic

Data Formats: Matlab

Tags: Lars, Lasso

Archive: download here

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