PCA(主成分分析)和特征选择之间的区别 [英] Difference between PCA (Principal Component Analysis) and Feature Selection

问题描述

机器学习中的主成分分析(PCA)和特征选择之间有什么区别? PCA是功能选择的一种手段吗?

What is the difference between Principal Component Analysis (PCA) and Feature Selection in Machine Learning? Is PCA a means of feature selection?

推荐答案

PCA是一种方法，可以找出哪些特征对于最佳描述数据集中的方差非常重要.它最常用于减少大数据集的维数，以便在原始数据本来就具有高维(例如图像识别)的情况下应用机器学习变得更加实用.

PCA is a way of finding out which features are important for best describing the variance in a data set. It's most often used for reducing the dimensionality of a large data set so that it becomes more practical to apply machine learning where the original data are inherently high dimensional (e.g. image recognition).

PCA有局限性，因为它依赖于要素元素之间的线性关系，并且在开始之前通常不清楚它们之间的关系.由于它还隐藏"了对数据差异贡献不大的特征元素，因此有时可以消除很小但很重要的差异因素，从而影响机器学习模型的性能.

PCA has limitations though, because it relies on linear relationships between feature elements and it's often unclear what the relationships are before you start. As it also "hides" feature elements that contribute little to the variance in the data, it can sometimes eradicate a small but significant differentiator that would affect the performance of a machine learning model.

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