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Kernel PCA and Pre-Image Reconstruction

4.8 | 12 ratings Rate this file 98 Downloads (last 30 days) File Size: 6.9 MB File ID: #39715 Version: 1.4
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Kernel PCA and Pre-Image Reconstruction


Quan Wang (view profile)


04 Jan 2013 (Updated )

standard PCA, Gaussian kernel PCA, polynomial kernel PCA, pre-image reconstruction

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In this package, we implement standard PCA, kernel PCA, and pre-image reconstruction of Gaussian kernel PCA. We also provide three demos: (1) Two concentric spheres embedding; (2) Face classification with PCA/kPCA; (3) Active shape models with kPCA.
Standard PCA is not optimized for very high dimensional data. But our kernel PCA implementation is very efficient, and has been used in many research projects.

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MATLAB release MATLAB 8.0 (R2012b)
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Comments and Ratings (21)
04 Dec 2016 Jayanth Reddy Regatti

Hi Wang, I have a small doubt. How do we define our own kernel here? I want to use an rbf kernel.

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06 Aug 2016 chen

chen (view profile)

13 Jun 2016 Mohammed Baydoun

13 Jun 2016 Mohammed Baydoun

09 Jun 2016 kumud arora

Why the test data kernel is not centralized??

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20 May 2016 Anamika jain

which type of data should be used to execute this code?

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14 Apr 2016 danhe

danhe (view profile)

16 Feb 2015 ait mohamed linda

what is the form of the database to use

08 Dec 2014 phil

phil (view profile)

Hi, Wang!
For several reasons, I need to do the KNN classifier after reducing the dimension of training and sampling data. When the type is guassian, it seems that the eigenvector will be complex number so that KNN is invaild. How can I solve this problem? Thanks so much.


23 Nov 2014 Yan wang

16 Jul 2014 mania

mania (view profile)

please help us to do fault detection in nonlinear systems with kernel pca. because i'm a starter in this field.

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18 Jun 2014 Quan Wang

Quan Wang (view profile)

Hi Cheung,

If you compute ||a-b||, you will have to use loops. MATLAB loops are slow, and distanceMatrix avoids using loops, thus is very fast.


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17 Jun 2014 Cheung

Cheung (view profile)


24 Apr 2014 Quan Wang

Quan Wang (view profile)

Dear all,

We have updated the code and the document. The code now generates exactly the same results as shown in the document. I am sorry for the confusion in previous versions of this package. The current version has lots of significant improvements.

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08 Mar 2014 Pradeesh

When running demo file why the 3rd fig is in 2d but not in 3d as plot3() is used to plot variable in 3d

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03 Mar 2014 Yan Ou

Yan Ou (view profile)

03 Mar 2014 Siqi

Siqi (view profile)

So Good!

03 Mar 2014 Siqi

Siqi (view profile)

05 Apr 2013 Quan Wang

Beside, I myself am using this code package (v2.0) for a number of research projects. I am pretty sure the code works well and has been well optimized.

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05 Apr 2013 Quan Wang

Hi Kris. The "paper" (actually a course project report) was using unordered eigenvalues, while in the updated code I have decreasingly ordered eigenvalues. So the code is more "correct" in a scientific sense. If you want to generate the same results as the report, you can uncomment
"% eigValue=eigValue(1:min(size(X)));"
in kPCA.m.

Hope this helps!

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04 Apr 2013 Kris Villez

Dear Quan Wang,

Thanks for sharing your code. However, I am not able to reproduce the results displayed in Figure 3 and 4 of your paper.

For Figure 3, it is not clear what the order of the applied polynomial is.

For Figure 4, the two features are highly correlated while the should in fact be uncorrelated. I wondered if this is a mistake in the paper or in the code.

Thanks for your comments,

25 Feb 2013 1.1

The efficiency is optimized.

24 Apr 2014 1.2

We replaces all demos, and the data used for the demo. We also updated the document to provide better illustration and better experiments. Now the code generates exactly the same results as shown in the paper.

24 Apr 2014 1.3

addpath('../code') in demo2

02 Sep 2014 1.4

Fixed a fatal bug in pre-image reconstruction.

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