The goal of supervised feature selection is to find a subset of input
features that are responsible for predicting output values. The least absolute
shrinkage and selection operator (Lasso) allows computationally efficient
feature selection based on linear dependency between input features and output
values. In this paper, we consider a feature-wise kernelized Lasso for
capturing non-linear input-output dependency. We first show that, with
particular choices of kernel functions, non-redundant features with strong
statistical dependence on output values can be found in terms of kernel-based
independence measures. We then show that the globally optimal solution can be
efficiently computed; this makes the approach scalable to high-dimensional
problems. The effectiveness of the proposed method is demonstrated through
feature selection experiments with thousands of features.

本文介绍了一种基于 Least Absolute Shrinkage 和 Selection Operator（Lasso）的特征选择方法，通过使用核函数来捕捉非线性的输入输出关系，并采用基于核的独立度量来找到与输出值具有强统计依赖关系的非冗余特征。实验结果表明，该方法在具有数千个特征的场景下具有很好的效果。