Employing SOM after PCA

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naghmeh moradpoor 2017-6-30

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评论： Greg Heath 2017-7-3

Hello,

I have applied PCA on my dataset and found out my first three variables together explained 93% of the total variance. So I decided to use only first 3 variables for SOM instead of 8 variables. I have total of 2643 observations in my dataset. There are two possible classification that I expect the SOM gives me but I don't know how to force SOM to do 2 classifications instead of 4. I use GUI for this and I would appreciate if anybody could help me with this.

Regards,

Naghmeh

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Answer 1

Greg Heath 2017-7-1

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编辑：Greg Heath 2017-7-1

在 MATLAB Online 中打开

1. PCA ranks principal components, not original variables. So, did you deduce 3 variables from the 3 principal components?

2. PLSREGRESS ranks original variables. Use that instead.

3. SOM is an unsupervised clustering algorithm that ignores classes, IT IS NOT A CLASSIFIER!

4. PATTERNNET is a NEURAL NETWORK SUPERVISED CLASSIFIER. If you are familiar with NNs, use that.

5. Otherwise, search the STATISTICS TOOLBOX for classifiers.

6 Meanwhile, search both NEWSGROUP and ANSWERS using

CLASSIFICATION

Hope this helps.

Thank you for formally accepting my answer

Greg

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Greg Heath 2017-7-3

Do not use PCA and/or SOM for supervised classification.

Greg

Greg Heath 2017-7-3

However, if you have UNLABELED data and want to divide it into clusters to determine where individual classes might be, SOM and PCA + SOM are appropriate.

Hope this is clear.

Greg

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