Description:

• DAL is an efficient and flexibible MATLAB toolbox for solving the following optimization problem:

  minimize f(Ax) + lambda*c(x)
  

  where A (m x n) is a design matrix, f is a loss function, and c is a measure of sparsity.

• DAL can handle your favorite (convex, smooth) loss functions (squared loss, logistic loss, etc).

• DAL can handle A (and its transpose) provided as function handles.

• DAL can handle several "sparsity" measures in an unified way. Currently L1, grouped L1, and trace norm (testing, requires PROPACK) measures are supported.

• DAL is efficient when m<<n (m: #samples, n: #unknowns) or the matrix A is poorly conditioned.

• DAL is fast when the solution is sparse but the matrix A can be dense.

• DAL is written in MATLAB.

Changes to previous version:

• 35% faster group lasso.
• Sparse connectivity inference example added (s_test_hsgl.m).
• Non-negative lasso (thanks to Shigeyuki Oba).
• Uses Mark Tygert's pca.m for SVD (PROPACK is not required anymore).

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Agnostic

Data Formats: Agnostic

Tags: Optimization, Trace Norm, Group Lasso, Lasso, Sparse Learning, L1 Regularization, Logistic Regression

Archive: download here

Comments

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.