我们应该始终首先执行功能归一化，然后降低功能？
有时执行功能降低会减少使用PCA等方法的功能数量，然后我们只能扩展相关变量。是否有规则首先进行归一化/缩放，然后进行降低？
Sometimes performing feature reduction reduces number of features with methods like PCA and then we could scale only the relevant variables. Is there a rule that we need to do normalization/scaling first and then the feature reduction?
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我建议首先对您的功能数据进行归一化/缩放，然后执行功能选择。这是因为大多数功能选择技术都需要有意义的数据表示。通过标准化数据，您的功能具有相同的数量级和分散的顺序，这使得更容易找到其中一种更相关。
例如，对于PCA，计算基于您功能的标准偏差（SD），以找到数据的新投影的相关轴。如果您不将数据归一化，则具有高SD的功能将具有较高的重量，而小型SD的功能会在计算PCA时扭曲其相关性。
I would suggest first do your normalization/scaling on your feature data and then performing feature selection. This is because most of the feature selection techniques require a meaningful representation of your data. By normalizing your data your features have the same order of magnitude and scatter, which makes it easier to find which one of those is more relevant.
For example, for PCA the computation is based on the standard deviation (SD) of your features to find the relevant axis of a new projection of your data. If you do not normalize your data, features with a high SD will have a higher weight compared to features with a small SD distorting their relevance when computing the PCA.