
TL;DR
This paper introduces a new stretchy polynomial regression method that modifies covariance computation with a power term, enhancing learning flexibility and effectiveness in compressive learning scenarios.
Contribution
It presents a novel closed-form solution for polynomial regression that incorporates a stretch factor, extending ridge regression techniques.
Findings
Effective on synthetic data
Improves compressive learning performance
Provides primal and dual closed-form solutions
Abstract
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.
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Taxonomy
TopicsSparse and Compressive Sensing Techniques · Anomaly Detection Techniques and Applications · Advanced Statistical Methods and Models
