What is the difference between multi-task lasso regression and ridge regression? The optimization function of multi-task lasso regression is m i n w ∑ l = 1 L 1 / N t ∑ i = 1 N t J l ( w , x , y ) + γ ∑ l = 1 L | | w l | | 2 while ridge regression is m i n w ∑ l = 1 L 1 / N t J l ( w , x , y ) + γ | | w l | | 2 which looks the same as the ridge regression. As for me, the problem of multi-task lasso regression is equivalent to solve global ridge regression. So what is the difference between these two regression methods? Both of them use L 2 function. Or does it mean that in multi-task lasso regression, the shape of W is (1,n)?

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What is the difference between multi-task lasso regression and ridge regression? The optimization function of multi-task lasso regression is  m  i      n    w        ∑          l      =      1        L    1  /      N    t        ∑          i      =      1                N      t            J    l    (  w  ,  x  ,  y  )  +  γ      ∑          l      =      1        L          |      |          w      l          |              |      2      while ridge regression is  m  i      n    w        ∑          l      =      1        L    1  /      N    t        J    l    (  w  ,  x  ,  y  )  +  γ        |      |          w      l          |              |      2      which looks the same as the ridge regression. As for me, the problem of multi-task lasso regression is equivalent to solve global ridge regression. So what is the difference between these two regression methods? Both of them use       L    2   function. Or does it mean that in multi-task lasso regression, the shape of   W is (1,n)?

iskakanjulc · Accepted Answer

Least Absolute Shrinkage and Selection Operator uses       L    1   regularization      min    x    f  (  x  )  +  λ  ‖  x      ‖    1    .

What is the difference between multi-task lasso regression and ridge regression? The optimization fu

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