isLouisHsu / isLouisHsu/isLouisHsu.github.io
PCA and LDA with Kernel | LOUIS' BLOG
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Description
https://louishsu.xyz/2019/09/05/PCA-and-LDA-with-Kernel/
前言核函数可将数据进行升维，在更高维度的空间进行数据分析，可以将线性不可分问题转换为线性可分问题，关于核函数，详细可查看Support Vector Machine 2.2核技巧。在PCA与LDA中分别介绍了主成分分析与线性鉴别分析，但这两种均为线性方法，本文利用核技巧将数据映射到高维空间，然后再用两种降维方法进行降维。 先升维再降维！ 原理假设有$M$个$N$维样本组成数据矩阵$X_{N \ti
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