LASSO Regression


LASSO stands for “Least Absolute Shrinkage and Selection Operator”. The term is often used synonymous with 1-norm regularization. In its most basic form it refers to 1-norm regularized linear regression. For the basics on linear regression we refer to the Linear Regression tutorial.

In contrast to plain linear regression or ridge regression LASSO solves a 1-norm regularized problem. For training data

\[S=\{(x_1, y_1), \dots, (x_l, y_l)\} ,\]

with vector valued inputs x and real valued labels y the following optimization problem is solved:

\[\min_w \,\, \lambda \|w\|_1 + \sum_i \|y_i - \langle w, x_i \rangle\|_2^2\]

The decisive property of LASSO regression is that the one-norm term enforces sparseness of the solution. In particular for rather large values of \(\lambda\) the solution w has only few non-zero components. This allows regression to be meaningful even if the feature dimension greatly exceeds the number of data points, since the method reduces the linear predictor to few variables. Therefore the method is often used for the identification of (hopefully) causal relationships: it is hoped that the label is mainly caused by the values of rather few features. These are often termed explanatory variables.

LASSO problems can be solved quickly with coordinate descent algorithms, see [FHHT2007]. Shark implements the optimized algorithm from [GU2013].

LASSO in Shark

The method in implemented by the LassoRegression trainer. It operates on a standard LinearModel:

#include <shark/Algorithms/Trainers/LassoRegression.h>

        double lambda = 1.0;

        LinearModel<> model;
        LassoRegression<> trainer(lambda);

        trainer.train(model, data);

Of course, data is assumed to be training data of appropriate type, i.e., RealVector inputs and one-dimensional regression labels. After training the weight vector of the linear model can be examined for non-zero coefficients and therefore for explanatory variables. Shark comes with a fully operational example program:



[FHHT2007]J. Friedman, T. Hastie, H. Höfling, and R. Tibshirani. Pathwise Coordinate Optimization. The Annals of Applied Statistics, 1(2):302-332, 2007.
[GU2013]T. Glasmachers and Ü. Dogan. Accelerated Coordinate Descent with Adaptive Coordinate Frequencies. In Proceedings of the fifth Asian Conference on Machine Learning (ACML), 2013.