Gaussian Kernel Regression for Multidimensional Feature with Matlab code

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Gaussian Kernel Regression for Multidimensional Feature with Matlab code (Gaussian Kernel or RBF Smoother)

I am sharing a Matlab code for Gaussian Kernel Regression algorithm for multidimensional input (feature).

In the previous post (link), I posted a theory of Gaussian Kernel Regression, and shared a Matlab code for one dimensional input. If you want to know about the theory, read the previous post. In the previous post, many visitors asked me for a multidimensional input version. Finally I made a Gaussian Kernel Regression Program for a general dimensional input

You can download the program from this link.

<Download>

I wrote a demo program to show how to use the code as easy as possible.

The below is the demo program, and a demo result plot. In this demo program, the dimension of input is 2 because of visualization, but it is expendable to an arbitrary dimension.

 

demo_codedemo_result

 

For the optimization of kernel bandwidth, see my other article <Link>.

 

I wish this program can save your time and effort for your work.

If you have any question, please leave a reply.

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I am Youngmok Yun, and writing about robotics theories and my research.

My main site is http://youngmok.com, and Korean ver. is  http://yunyoungmok.tistory.com.

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4 thoughts on “Gaussian Kernel Regression for Multidimensional Feature with Matlab code”

  1. Pingback: Gaussian kernel regression with Matlab code (Gaussian Kernel or RBF Smoother) | Youngmok Yun: Roboticist in The Univ. of Texas at Austin

  2. Pingback: Gaussian Kernel Bandwidth Optimization with Matlab Code | Youngmok Yun: Roboticist in The Univ. of Texas at Austin

  3. Shahid Mahmood

    s = randn(1000,1);
    a=randn(100,1000);
    x=a*s;
    Can you explain to me in matlab using above dimension how to calculate gaussian kernel matrix k_αα [ α_1,α_2,⋯,α_i⋯ α_(c-1),α_c ]∈ R^(m×n) kernel matrix having (i,j)^th entry is k(α_i,α_j), and k_α∈ R^(m×1) having entries k(α_i,x).

    Reply ↓

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