I try to apply non-linear dimension reduction in R. As usual in machine learning I have a large data set (100 K rows). I tried the packages
library( RDRToolbox ) res.lle = LLE(as.matrix(temp), dim = 3 , k = 10) res.iso = Isomap(as.matrix(temp), dim = 3 , k = 10) library( vegan ) isomap(dist(temp), ndim=10)
The first algoritms use the distance matrix internally and crash as they want to allocate 32GB. The second approach needs the distance matrix and crashes too.
Are there any tricks to do non-linear dimension reduction either with these algorithms? Or are there packages that do not use the raw and full distance matrix? Such approaches have to crash in my setting on my machine.
The only algorithm that works for me is
Rtsne that appearantly does not use the whole distance matrix.
largeVis was too slow so far to let it finish.