为什么使用奇异值SVD调整svm算法极其缓慢？

如何解决为什么使用奇异值SVD调整svm算法极其缓慢？

我有300,000个变量和大约5000行的大数据。通过使用RSpectra进行奇异值分解，我获得了300个奇异值。使用这300个变量通过超参数调整运行svm变得异常缓慢。 24GB RAM计算机花费了超过17个小时。当我使用包含60,000个变量和5000行的文档特征矩阵（dfm）来运行该算法时，该算法的运行速度要快得多。

library(doMC)
start_time <- Sys.time()
registerDoMC(cores=5) 
library(e1071)
set.seed(123) #for reproducibility

svm_tuned_upsample <- tune(svm,train.x = train_svd_df[,-1],train.y = as.factor(train_svd_df$Include),kernel = "radial",type   = "C-classification",parallel= TRUE,ranges=list(cost=c(0.001,0.01,0.1,0.2,0.3,0.4,0.5,1,5,6,7,8,10,15),gamma=c(0.0009,0.001,0.002,0.003,0.0035,0.004,0.0045,0.005)),validation.x=tune.control(sampling = "cross",cross=10)
                          ) 

Sys.time() - start_time