Curating the database for analysis with both t-SNE and PCA

Labels<-train_Data\(V1 train_Data\)V1<-as.factor(train_Data\(V1) ## for plotting colors = rainbow(length(unique(train_Data\)V1))) names(colors) = unique(train_Data$V1)

Executing the algorithm on curated data

tsne <- Rtsne(train_Data[,-1], dims = 2, perplexity=30, verbose=TRUE, max_iter = 500) exeTimeTsne<- system.time(Rtsne(train[,-1], dims = 2, perplexity=30, verbose=TRUE, max_iter = 500))

Plotting

plot(tsne\(Y, t='n', main="tsne") text(tsne\)Y, labels=train_Data\(V1, col=colors[train_Data\)V1])