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The mathematical details of using regression kernel for incremental learning

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Yasmine
Yasmine 2024 年 5 月 13 日
コメント済み: Yasmine 2024 年 5 月 18 日
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
Drew 2024 年 5 月 15 日
編集済み: Drew 2024 年 5 月 15 日
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.
  3 件のコメント
Drew
Drew 2024 年 5 月 15 日
編集済み: Drew 2024 年 5 月 15 日
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
Yasmine
Yasmine 2024 年 5 月 18 日
The information I am asking about is not directly available, but I think with reading deep into the link you provided here, I will be able to extract it

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