Employing SOM after PCA

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naghmeh moradpoor
naghmeh moradpoor le 30 Juin 2017
Commenté : Greg Heath le 3 Juil 2017
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

Réponse acceptée

Greg Heath
Greg Heath le 1 Juil 2017
Modifié(e) : Greg Heath le 1 Juil 2017
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
  3 commentaires
Greg Heath
Greg Heath le 3 Juil 2017
Do not use PCA and/or SOM for supervised classification.
Greg
Greg Heath
Greg Heath le 3 Juil 2017
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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