Project details for DAL

Logo DAL 1.1

by ryota - February 18, 2014, 19:07:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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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:
  • Supports weighted lasso (dalsqal1.m, dallral1.m)
  • Supports weighted squared loss (dalwl1.m)
  • Bug fixes (group lasso and elastic-net-regularized logistic regression)
BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
URL: Project Homepage
Supported Operating Systems: Agnostic
Data Formats: Agnostic
Tags: Optimization, Trace Norm, Group Lasso, Lasso, Sparse Learning, L1 Regularization, Logistic Regression
Archive: download here

Other available revisons

Version Changelog Date
1.1
  • Supports weighted lasso (dalsqal1.m, dallral1.m)
  • Supports weighted squared loss (dalwl1.m)
  • Bug fixes (group lasso and elastic-net-regularized logistic regression)
February 18, 2014, 19:07:06
1.05
  • 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).
May 3, 2011, 07:00:43
1.01
  • Logistic loss: : dallrl1.m, dallrgl.m, dallrds.m
  • Unequal-sized blocks supported in Group lasso regularization
  • eta: initial eta=0.01/lambda
  • dallrds.m: trace-norm regularized logistic regression (requires PROPACK)
December 14, 2009, 09:43:50
0.97

Initial Announcement on mloss.org.

April 13, 2009, 09:39:59

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