IEEE Trans Pattern Anal Mach Intell. 2017 Dec;39(12):2395-2408. doi: 10.1109/TPAMI.2017.2648792. Epub 2017 Jan 5.
Principal Component Analysis (PCA) is a powerful and widely used tool for dimensionality reduction. However, the principal components generated are linear combinations of all the original variables and this often makes interpreting results and root-cause analysis difficult. Forward Selection Component Analysis (FSCA) is a recent technique that overcomes this difficulty by performing variable selection and dimensionality reduction at the same time. This paper provides, for the first time, a detailed presentation of the FSCA algorithm, and introduces a number of new variants of FSCA that incorporate a refinement step to improve performance. We then show different applications of FSCA and compare the performance of the different variants with PCA and Sparse PCA. The results demonstrate the efficacy of FSCA as a low information loss dimensionality reduction and variable selection technique and the improved performance achievable through the inclusion of a refinement step.
主成分分析（PCA）是一种强大且广泛应用的降维工具。然而，生成的主成分是所有原始变量的线性组合，这通常使得解释结果和根本原因分析变得困难。前向选择成分分析（FSCA）是一种最近的技术，它通过同时进行变量选择和降维来克服这个困难。本文首次详细介绍了 FSCA 算法，并引入了一些新的 FSCA 变体，这些变体包含一个改进步骤以提高性能。然后，我们展示了 FSCA 的不同应用，并将不同变体与 PCA 和稀疏 PCA 的性能进行了比较。结果表明，FSCA 作为一种低信息损失的降维和变量选择技术是有效的，并且通过包含改进步骤可以实现性能的提高。
