The best analogy I have for the PCA plot is shining a flashlight through a snow globe and looking at the shadows on a wall. Example of 3D to 2D dimensional reduction. If points are close together in 3D they will not move much in relation to each other in 2D as the globe is rotated. Same should apply for 10KD to 2D.
In our case the apply( ) and prcomp( ) functions are doing all the work. I haven't been able to figure out how to control rotation using them so far.
I recently acquired a VR headset. I'm wondering how to do PCA in 3D, i.e. decompose 10KD to 3D. If we can come up with an algorithm I'll code it it CUDA for real time 3D viewing and rotation in VR. Think Minority Report for language comparison.
v = t(as.matrix(read.table(“langvec.txt”,sep=”,”)))
v = v[,which(apply(v,2,var)!=0)]
v.pca <- prcomp(v,scale. = TRUE, center = TRUE)
fviz_pca_biplot(v.pca, invisible = “var”,title=”Hypervector Language Comparison PCA Plot”,repel=TRUE,axes.linetype = “blank”)
In our case the apply( ) and prcomp( ) functions are doing all the work. I haven't been able to figure out how to control rotation using them so far.
I recently acquired a VR headset. I'm wondering how to do PCA in 3D, i.e. decompose 10KD to 3D. If we can come up with an algorithm I'll code it it CUDA for real time 3D viewing and rotation in VR. Think Minority Report for language comparison.
v = t(as.matrix(read.table(“langvec.txt”,sep=”,”)))
v = v[,which(apply(v,2,var)!=0)]
v.pca <- prcomp(v,scale. = TRUE, center = TRUE)
fviz_pca_biplot(v.pca, invisible = “var”,title=”Hypervector Language Comparison PCA Plot”,repel=TRUE,axes.linetype = “blank”)