论文标题
HARFE：硬径随机功能扩展
HARFE: Hard-Ridge Random Feature Expansion
论文作者
论文摘要
我们提出了一个随机特征模型，用于近似称为Hard-Ridge随机特征扩展方法（HARFE）的高维稀疏添加功能。该方法利用用于稀疏山脊回归（SRR）问题的基于硬质追击的算法来近似于随机特征矩阵的系数。 SRR公式平衡在获得稀疏模型中使用较少术语的稀疏模型和基于山脊的平滑度往往对噪声和异常值稳健。此外，我们在随机特征矩阵中使用随机稀疏连接模式来匹配加法函数假设。我们证明，根据稀疏山脊回归模型的噪声和参数，HARFE方法可以通过给定的误差收敛。基于合成数据和实际数据集的数值结果，HARFE方法比其他最先进的算法获得了较低（或可比）的错误。
We propose a random feature model for approximating high-dimensional sparse additive functions called the hard-ridge random feature expansion method (HARFE). This method utilizes a hard-thresholding pursuit-based algorithm applied to the sparse ridge regression (SRR) problem to approximate the coefficients with respect to the random feature matrix. The SRR formulation balances between obtaining sparse models that use fewer terms in their representation and ridge-based smoothing that tend to be robust to noise and outliers. In addition, we use a random sparse connectivity pattern in the random feature matrix to match the additive function assumption. We prove that the HARFE method is guaranteed to converge with a given error bound depending on the noise and the parameters of the sparse ridge regression model. Based on numerical results on synthetic data as well as on real datasets, the HARFE approach obtains lower (or comparable) error than other state-of-the-art algorithms.
