Feature Extraction Algorithms (FEAs) aim to address the curse of dimensionality that makes machine learning algorithms incompetent. Our study conceptually and empirically explores the most representative FEAs. First, we review the theoretical background of many FEAs from different categories (linear vs. nonlinear, supervised vs. unsupervised, random projection-based vs. manifold-based), present their algorithms, and conduct a conceptual comparison of these methods. Secondly, for three challenging binary and multi-class datasets, we determine the optimal sets of new features and assess the quality of the various transformed feature spaces in terms of statistical significance and power analysis, and the FEA efficacy in terms of classification accuracy and speed.

特征提取算法（FEA）旨在解决导致机器学习算法无法胜任的维度诅咒。我们的研究从概念和经验上探索了最具代表性的有限元分析。首先，我们回顾了来自不同类别（线性与非线性，有监督与无监督，基于随机投影与基于流形）的许多有限元分析的理论背景，介绍了它们的算法，并对这些方法进行了概念比较。其次，对于三个具有挑战性的二元和多类数据集，我们确定最佳的新特征集，并根据统计显着性和功效分析评估各种变换特征空间的质量，并根据分类准确性和速度评估FEA功效。