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[R] PCA kernels for data types

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.

submitted by /u/sap218
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.