I read somewhere that kernels for kPCA can be used for different data types.

I used PCAmix (R package: classic PCA on continuous and MCA on categorical then combines) on my data set and my data doesn’t split in any way – PC1 and PC2 is just a ball of coordinates.

So I was thinking of trying two different kernels for data types then combining them?

My supervisor isn’t listening when I tell him that there is no variance in our data but he is determined to find something so I’m looking into a lot of different dimension reduction methods.