Deterministic Stretchy Regression
This is an incremental improvement for compressive learning in high-dimensional data analysis.
The paper tackles the problem of high-dimensional data in compressive learning by introducing a ridge regression extension with stretchable parameters, and demonstrates its effectiveness through empirical evaluation on synthetic and real-world data.
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.