Feature Understanding and Sparsity Enhancement via 2-Layered kernel machines (2L-FUSE)
Fabiana Camattari, Sabrina Guastavino, Francesco Marchetti, Emma Perracchione
我们提出了一种新的回归任务的间距增强策略,基于通过2层内核机器学习数据自适应内核度量,即形状矩阵。 由此产生的形状矩阵定义了输入空间的Mahalanobis型变形,然后通过特征分解进行因子分解,使我们能够确定特征空间中信息最丰富的方向。 这种数据驱动的方法提供了一个灵活、可解释和准确的特征还原方案。 合成和应用于地磁风暴真实数据集的数字实验表明,我们的方法实现了最小但高度信息化的特征集,而不会失去预测性能。
We propose a novel sparsity enhancement strategy for regression tasks, based on learning a data-adaptive kernel metric, i.e., a shape matrix, through 2-Layered kernel machines. The resulting shape matrix, which defines a Mahalanobis-type deformation of the input space, is then factorized via an eigen-decomposition, allowing us to identify the most informative directions in the space of features. This data-driven approach provides a flexible, interpretable and accurate feature reduction scheme. N...
