Stretchy Polynomial Regression
This is an incremental improvement for regression tasks, potentially benefiting data analysis in compressive learning contexts.
The paper tackles the problem of stretchy polynomial regression learning by introducing a solution with primal and dual closed-forms that stretch covariance computation via a power term to compress or amplify estimation, showing effectiveness in experiments on synthetic and real-world data for compressive learning.
This article proposes a novel solution for stretchy polynomial regression learning. The solution comes in primal and dual closed-forms similar to that of ridge regression. Essentially, the proposed solution stretches the covariance computation via a power term thereby compresses or amplifies the estimation. Our experiments on both synthetic data and real-world data show effectiveness of the proposed method for compressive learning.