LARS stands for Least Angle Regression. It is a feature selection technique for multiple regression that incorporates a penalty.

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CV for LASSO tuning parameter using LARS

If I use the LARS algorithm to fit the LASSO path, is it sufficient to cross-validate using the values of $\lambda$ at each step in LARS or is it better to use a finer grid of $\lambda$ values? I ...
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Using Leave-One-Out Cross Validation with LARS

I have a kind of obscure question about using the Least Angle Regression (LARS) algorithm for variable selection. If I'm understanding it right, my professor formulates LARS as such: $$\mathbb{min}\ ...
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Dataset for Least Angle Regression

I have read that least angle regression is good for high dimensional data. I didn't actually understand the meaning of high dimensional data, so does this mean $p>>n$ case? And does anyone know ...
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What is a least angle regression?

Conceptually, I don't understand what is a least angle regression and why it solves LASSO http://www.cc.gatech.edu/~isbell/reading/papers/lasso_simple.html.pdf We know that LASSO is $$\min_x||Ax - ...
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Parameter tuning in lars (lasso) matlab

I am trying to use lars (matlab implementation:http://www.ece.ubc.ca/~xiaohuic/code/LARS/LARS.htm). I want to do a leave one out cross validation on my data using this code. I have the following ...
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How to add a non-negative constraint to lasso4j?

Lasso4j is a Java implementation of the Lasso L1-constrained fitting for linear regression. I would like to add a non-negativity constraint on the weights, meaning that the non-zero sparse ...
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What if Lasso selects transformed terms but not untransformed terms

Suppose I have standard normal features $X_i \in \{X_i : i \in \{1,...,1000\}\}$. I extend this set of predictors with transformations as follows: $\{X_i,X_i^2,X_iI(X_i > 0) : i \in ...
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Using LASSO from lars (or glmnet) package in R for variable selection

Sorry if this question comes across a little basic. I am looking to use LASSO variable selection for a multiple linear regression model in R. I have 15 predictors, one of which is categorical(will ...
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Least angle regression packages for R or MATLAB

I am looking for a Least Angle Regression (LAR) packages in R or MATLAB which can be used for classification problems. The only package that I currently know which fits this description is glmpath. ...
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'Forward Stagewise' option in LARS algorithm

Can anyone help me understand the forward stagewise part in the LARS algorithm? I was reading the R code and could not figure out what is ...
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Max steps in lars

I don't know how many steps are necessary for lars() to select the variables till the algorithm proceeds to the saturated fit (especially using the ...
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Constant signs of correlation in the active set in least angle regression

I am trying to comprehend the proof of the Least Angle Regression algorithm and I am stuck at certain points. I would appreciate any help that I can get. Let me set the stage: I am following the ...
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LARS - LASSO with weights

I am interested in solving the following problem $$ \min_{\boldsymbol{\beta}} \left( \mathbf{y}-\mathbf{X}\boldsymbol{\beta} \right)^T W \left( \mathbf{y}-\mathbf{X}\boldsymbol{\beta} \right) + ...
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Advantages of doing “double lasso” or performing lasso twice?

I once heard a method of using the lasso twice (like a double-lasso) where you perform lasso on the original set of variables, say S1, obtain a sparse set called S2, and then perform lasso again on ...
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LASSO/LARS vs general to specific (GETS) method

I have been wondering, why are LASSO and LARS model selection methods so popular even though they are basically just variations of step-wise forward selection (and thus suffer from path dependency)? ...
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What problem do shrinkage methods solve?

The holiday season has given me the opportunity to curl up next to the fire with The Elements of Statistical Learning. Coming from a (frequentist) econometrics perspective, I'm having trouble grasping ...
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Feature selection with k-fold cross-validated least angle regression

I am using the least angle regression (LARS) to extract the most important predictors ($x_1, x_2,...,x_p$) for my response variable ($y$). I have seven predictors ($x_1,x_2,...,x_7$) for each ...