The mathematical details of using regression kernel for incremental learning
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You state that in incremental learning using regression kernel "binary Gaussian kernel regression model for incremental learning. The kernel model maps data in a low-dimensional space into a high-dimensional space, then fits a linear model in the high-dimensional space."
I am writing a paper so I need mathematical details of mapping that maps data to high dimensional space
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Drew
el 15 de Mayo de 2024
Editada: Drew
el 15 de Mayo de 2024
The documentation page that you quoted has an "Algorithms" section, and a set of references. You will likely find the answers you need in those places. See: https://www.mathworks.com/help/stats/incrementalregressionkernel.html#mw_7228730b-3b97-423b-b291-375152326425
If this answer helps you, please remember to accept the answer.
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Drew
el 15 de Mayo de 2024
Editada: Drew
el 15 de Mayo de 2024
Check the "More About" section of the corresponding doc page, fitrkernel, which has a section on "Random Feature Expansion", describing the mathematics for the feature expansion:
For more beyond that, the references for fitrkernel and incrementalRegressionKernel have more info.
You can also use "open incrementalRegressionKernel.m" at the MATLAB prompt to read the m-file help which has a few examples referencing fitrkernel:
open incrementalRegressionKernel.m
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