| 摘要 |
A method for training a classifier for selecting features in sparse data sets with high feature dimensionality includes providing a set of data items x and labels y, minimizing a functional of the data items x and associated labels y L  ( w , b , a , c , γ 1 , γ 2 ) := 1 N  ∑ i = 1 N  a i + λ 1   c  1 + λ 2 2   w  2 2 + γ 1 T  ( e - Y  ( Xw + be ) - a ) + γ 2 T  ( w - c ) + μ 1 2   e - Y  ( Xw + be ) - a  2 2 + μ 2 2   w - c  2 2 to solve for hyperplane w and offset b of a classifier by successively iteratively approximating w and b, auxiliary variables a and c, and multiplier vectors γ1 and γ2, wherein λ1, λ2, μ1, and μ2 are predetermined constants, e is a unit vector, and X and Y are respective matrix representations of the data items x and labels y; providing non-zero elements of the hyperplane vector w and corresponding components of X and Y as arguments to an interior point method solver to solve for hyperplane vector w and offset b, wherein w and b define a classifier than can associate each data item x with the correct label y.
|
