lars.hpp (3)

Classes

class mlpack::regression::LARS

An implementation of LARS, a stage-wise homotopy-based algorithm for l1-regularized linear regression (LASSO) and l1+l2 regularized linear regression (Elastic Net).

Namespaces

mlpack

Linear algebra utility functions, generally performed on matrices or vectors. mlpack::regression

Regression methods.

Author:

Nishant Mehta (niche)

Definition of the LARS class, which performs Least Angle Regression and the LASSO.

Only minor modifications of LARS are necessary to handle the constrained version of the problem:

\[ \min_{\beta} 0.5 || X \beta - y ||_2^2 + 0.5 \lambda_2 || \beta ||_2^2 \] subject to $ ||\beta||_1 <= \tau $

Although this option currently is not implemented, it will be implemented very soon.

This file is part of MLPACK 1.0.10.

MLPACK is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

MLPACK is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details (LICENSE.txt).

You should have received a copy of the GNU General Public License along with MLPACK. If not, see http://www.gnu.org/licenses/.

Definition in file lars.hpp.

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