1.思路
2.代码
PCA <- function(data,k){#中心化处理standardize = data.frame()for(i in 1:nrow(data)){for(j in 1:ncol(data)){standardize[i,j] = data[i,j]-mean(data[,j])}}#协方差矩阵cov_data = data.frame()cov_data =cov(standardize)#求特征值、特征向量eig = eigen(cov_data)e = as.matrix(eig$vectors) #特征向量矩阵EigenVectors=as.matrix(e[,1:k]) #提取前k个standardize = as.matrix(standardize) result = standardize%*%EigenVectorsreturn(result)}#测试 data<-data.frame(X1<-c(2.5,0.5,2.2,1.9,3.1,2.3,2,1,1.5,1.1),X2<-c(2.4,0.7,2.9,2.2,3.0,2.7,1.6,1.1,1.6,0.9))
PCA(data,1)
data2 = data.frame(x1 = c(2,4,-5,6,8),x2 = c(5,4,7,15,3.5),x3 = c(1.2,3.5,5,14,2))PCA(data2,1)
PCA(data2,2)
