05.Least Angle Regression

Least Angle Regression

  1. Algorithm 3.2
  2. We want to change $\beta$ so that the prediction is closer to data $y$, i.e., we require the change of $\beta$ decreases $X\beta = y - r$. So the change should be $\propto X^T r $.
  3. Why this works? It reduces the MSE.
  4. LAR is similar to lasso.
  5. Modified LAR Algorithm 3.2a leads to lasso result.
  6. LAR(lasso) is efficient. It takes $\mathrm{min}(p,N-1)$ steps where lasso itself might take more than p steps.
  7. LAR and lasso are almost identical if we use the geometric meaning of the algorithms. But when some coefficient crosses 0, the differences pop up.
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