特征提取和更高的灵敏度

Question

在WBCD数据集上进行特征提取（PCA和LDA）后进行logistic回归，灵敏度得到了提高，但精度有所不同。我一直在寻找可以解释/研究特征提取如何提高分类器灵敏度的文献，但我什么也找不到。你知道吗

Answer 1

特征提取降低了数据的维数。这通常是为了创建一个更小的系统（以减少计算开销）和/或减少噪声（以获得更清晰的信号）。你知道吗

关于无监督学习（p.373），在统计学习导论中有一个简明的介绍，我想这就是你想要的。你知道吗

以PCA为例。统计学习导论：

When faced with a large set of correlated variables, principal components allow us to summarize this set with a smaller number of representative variables that collectively explain most of the variability in the original set. The principal component directions are presented in Section 6.3.1 as directions in feature space along which the original data are highly variable. These directions also define lines and subspaces that are as close as possible to the data cloud. To perform principal components regression, we simply use principal components as predictors in a regression model in place of the original larger set of variables.

Principal component analysis (PCA) refers to the process by which principal components are computed, and the subsequent use of these components in understanding the data. PCA is an unsupervised approach, since it involves only a set of features X1, X2,...,Xp, and no associated response Y. Apart from producing derived variables for use in supervised learning problems, PCA also serves as a tool for data visualization (visualization of the observations or visualization of the variables). We now discuss PCA in greater detail, focusing on the use of PCA as a tool for unsupervised data exploration, in keeping with the topic of this chapter.

我的go-to资源是统计学习的元素（这是免费提供的here）。534页以后有一个PCA的详细讨论，将其应用于手写，使问题更容易处理。你知道吗