PCA and automatic encoder for dimension reduction of R language

Principal component analysis
PCA Data frames are reduced by orthogonally transforming the data into a set of principal components . The first principal component explains the maximum variation of data in a single component , The second principal component explains the second largest variation , And so on . By selecting the top ķ explain 80-90% Changing principal components , Other ingredients can be deleted , Because they have no significant benefit to the model . This process preserves some potential information in the principal components , Help build better models .

PCA The program will be applied to the continuous variables on this data set .

suppressPackageStartupMessages(library(DAAG))
head(ais)
# standardise
minmax <- function(x