|Code Listing by Ambarish Jash|
The Gaussian Kernel can be changed to any desired kernel. However such a change will not dramatically improve results. This is a variant of ridge regression using the kernel trick (Mercers Theorem).
The different low dimensional embeddings are an orthonormal coordinate system generated from a
1. Diffusion process defined on the data
2 . Normalized Laplace Beltrami operator
3. Normalized Focker Plank operator
The correlation function calculated from one realization of an ensemble is inherently flawed since the expectation operation does not come into play. Hence it is important to have an idea of the variance in the correlation function.
This technique takes advantage of the kernel trick that can be used in PCA. This is a tutorial only and is slow for large data sets.
In line 30 the kernel can be changed. Any Kernel should do it.
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