Timeline for Geometric interpretation of penalized linear regression
Current License: CC BY-SA 3.0
3 events
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| Jun 19, 2012 at 19:30 | comment | added | JohnRos | Intuitively: Say you are minimizing the penalized sum of squares a second time. The sum of squares at the second minimization is smaller than the sum of squares of the first minimization. The relative importance of the penalized coefficients' norm will increase, i.e., there is more to be gained by shrinking the coefficients some more. Ridge regression is a good example in which you have a nice closed form for the hat matrix and you can directly check if it is idempotent. | |
| Jun 19, 2012 at 11:05 | comment | added | Lucas Reis | I can't see why it's not idempotent: if I project the vector in the space (even if it's not orthogonal projection), and I put a constraint in the coefficients, why would a new projection of this projected vector be different from the previous one? | |
| Jun 18, 2012 at 22:08 | history | answered | JohnRos | CC BY-SA 3.0 |