  An extension of the regularized least-squares in which the estimation
parameters are stretchable is introduced and studied in this paper. The
solution of this ridge regression with stretchable parameters is given in
primal and dual spaces and in closed-form. Essentially, the proposed solution
stretches the covariance computation by a power term, thereby compressing or
amplifying the estimation parameters. To maintain the computation of power root
terms within the real space, an input transformation is proposed. The results
of an empirical evaluation in both synthetic and real-world data illustrate
that the proposed method is effective for compressive learning with
high-dimensional data.
